CRNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "047cd5f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import string,cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f682dfce",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.sequence import pad_sequences"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "877f94c1",
   "metadata": {},
   "source": [
    "TỔNG QUÁT MÔ HÌNH SẼ XÂY DỰNG\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "[Tham Khao bai viet](https://towardsdatascience.com/build-a-handwritten-text-recognition-system-using-tensorflow-2326a3487cd5)\n",
    "\n",
    "[Tham Khao Code](https://github.com/TheAILearner/A-CRNN-model-for-Text-Recognition-in-Keras/blob/master/CRNN%20Model.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f9455b92",
   "metadata": {},
   "outputs": [],
   "source": [
    "fh = open('words.txt','r')\n",
    "connets = fh.readlines()\n",
    "lines = [line.strip() for line in connets]\n",
    "lines = lines[18:] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "228a4f4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a01-000u-00-00 ok 154 408 768 27 51 AT A',\n",
       " 'a01-000u-00-01 ok 154 507 766 213 48 NN MOVE',\n",
       " 'a01-000u-00-02 ok 154 796 764 70 50 TO to',\n",
       " 'a01-000u-00-03 ok 154 919 757 166 78 VB stop']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "25b731c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode_to_labels(txt):\n",
    "    # encoding each output word into digits\n",
    "    dig_lst = []\n",
    "    for index, char in enumerate(txt):\n",
    "        try:\n",
    "            dig_lst.append(char_list.index(char))\n",
    "        except:\n",
    "            print(char)\n",
    "        \n",
    "    return dig_lst\n",
    "char_list = \"!\\\"#&'()*+,-./0123456789:;?ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b6628587",
   "metadata": {},
   "outputs": [],
   "source": [
    "images = []\n",
    "labels = []\n",
    "\n",
    "RECORDS_COUNT = 10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "56edf3b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = []\n",
    "train_labels = []\n",
    "train_input_length = []\n",
    "train_label_length = []\n",
    "train_original_text = []\n",
    "\n",
    "valid_images = []\n",
    "valid_labels = []\n",
    "valid_input_length = []\n",
    "valid_label_length = []\n",
    "valid_original_text = []\n",
    "\n",
    "inputs_length = []\n",
    "labels_length = []"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "6f6f7f8f",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f5294a9",
   "metadata": {},
   "source": [
    "Đầu vào mạng CNN: Nó là bức ảnh xám có kích thước là 128 * 32. Thông thường, bức ảnh trong tâp dataset nó thực sự không cùng size (Không bị biến dạng), do đó ta cần resize nó lại. Ta sẽ sao chép hình ảnh vào một hình ảnh đích (màu trắng) có kích thước 128 × 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2d2a7406",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def process_image(img):\n",
    "    \"\"\"\n",
    "    Converts image to shape (32, 128, 1) & normalize\n",
    "    \"\"\"\n",
    "    w, h = img.shape\n",
    "    new_w = 32\n",
    "    new_h = int(h * (new_w / w))\n",
    "    img = cv2.resize(img, (new_h, new_w))\n",
    "    w, h = img.shape\n",
    "    img = img.astype('float32')\n",
    "    \n",
    "    # Converts each to (32, 128, 1)\n",
    "    if w < 32:\n",
    "        add_zeros = np.full((32-w, h), 255)\n",
    "        img = np.concatenate((img, add_zeros))\n",
    "        w, h = img.shape\n",
    "    \n",
    "    if h < 128:\n",
    "        add_zeros = np.full((w, 128-h), 255)\n",
    "        img = np.concatenate((img, add_zeros), axis=1)\n",
    "        w, h = img.shape\n",
    "        \n",
    "    if h > 128 or w > 32:\n",
    "        dim = (128,32)\n",
    "        img = cv2.resize(img, dim)\n",
    "    \n",
    "    img = cv2.subtract(255, img)\n",
    "    \n",
    "    img = np.expand_dims(img, axis=2)\n",
    "    \n",
    " \n",
    "    img = img / 255\n",
    "    \n",
    "    return img"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "742dcb53",
   "metadata": {},
   "source": [
    "Trong file words.txt nó chứa các thông tin bức ảnh đó\n",
    "\n",
    "'a01-000u-00-00 ok 154 408 768 27 51 AT A'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "122e0cce",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Ta install dataset IAM, giải nén các thư mục assic.tgz,words.tgz và có file ảnh và .txt format cấu trúc data\n",
    "\"\"\"\n",
    "max_label_len = 0\n",
    "\n",
    "for index, line in enumerate(lines):\n",
    "    splits = line.split(' ')\n",
    "    status = splits[1]\n",
    "    \n",
    "    if status == 'ok':\n",
    "        word_id = splits[0]\n",
    "        word = \"\".join(splits[8:])\n",
    "        \n",
    "        splits_id = word_id.split('-')\n",
    "        filepath = 'C:/Users/nguye/OneDrive/Code/handwritting_text/dataset/{}/{}-{}/{}.png'.format(splits_id[0], \n",
    "                                                  splits_id[0], \n",
    "                                                  splits_id[1], \n",
    "                                                  word_id)\n",
    "        \n",
    "        img = cv2.imread(filepath, cv2.IMREAD_GRAYSCALE)\n",
    "        try:\n",
    "            img = process_image(img)\n",
    "        except:\n",
    "            continue\n",
    "        try:\n",
    "            label = encode_to_labels(word)\n",
    "        except:\n",
    "            continue\n",
    "        if index % 10 == 0:\n",
    "            valid_images.append(img)\n",
    "            valid_labels.append(label)\n",
    "            valid_input_length.append(31)\n",
    "            valid_label_length.append(len(word))\n",
    "            valid_original_text.append(word)\n",
    "        else:\n",
    "            train_images.append(img)\n",
    "            train_labels.append(label)\n",
    "            train_input_length.append(31)\n",
    "            train_label_length.append(len(word))\n",
    "            train_original_text.append(word)\n",
    "        \n",
    "        if len(word) > max_label_len:\n",
    "            max_label_len = len(word)\n",
    "    \n",
    "    if index >= RECORDS_COUNT:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "851fb79b",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_padded_label = pad_sequences(train_labels, \n",
    "                             maxlen=max_label_len, \n",
    "                             padding='post',\n",
    "                             value=len(char_list))\n",
    "\n",
    "valid_padded_label = pad_sequences(valid_labels, \n",
    "                             maxlen=max_label_len, \n",
    "                             padding='post',\n",
    "                             value=len(char_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9d1e68e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((15700, 16), (1752, 16))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_padded_label.shape,valid_padded_label.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cf8789c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = np.array(train_images)\n",
    "train_input_length = np.array(train_input_length)\n",
    "train_label_length = np.array(train_label_length)\n",
    "\n",
    "valid_images = np.array(valid_images)\n",
    "valid_input_length = np.array(valid_input_length)\n",
    "valid_label_length = np.array(valid_label_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e8cff2a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((15700, 32, 128, 1), (1752, 32, 128, 1))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape,valid_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fd2011c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('Input_Output/train_images.npy', 'wb') as f:\n",
    "    np.save(f, train_images)\n",
    "with open('Input_Output/train_input_length.npy', 'wb') as f:\n",
    "    np.save(f, train_input_length)    \n",
    "with open('Input_Output/train_label_length.npy', 'wb') as f:\n",
    "    np.save(f, train_label_length)\n",
    "\n",
    "with open('Input_Output/train_padded_label.npy', 'wb') as f:\n",
    "    np.save(f, train_padded_label)    \n",
    "with open('Input_Output/valid_padded_label.npy', 'wb') as f:\n",
    "    np.save(f, valid_padded_label)    \n",
    "\n",
    "    \n",
    "with open('Input_Output/valid_images.npy', 'wb') as f:\n",
    "    np.save(f, valid_images)\n",
    "with open('Input_Output/valid_input_length.npy', 'wb') as f:\n",
    "    np.save(f, valid_input_length)\n",
    "with open('Input_Output/valid_label_length.npy', 'wb') as f:\n",
    "    np.save(f, valid_label_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "270e13a3",
   "metadata": {},
   "source": [
    "Kiến truc mạng sẽ tham khảo từ [Paper](https://arxiv.org/pdf/1507.05717.pdf) này"
   ]
  },
  {
   "attachments": {
    "archtecture.jpg": {
     "image/jpeg": "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"
    }
   },
   "cell_type": "markdown",
   "id": "30db8c1f",
   "metadata": {},
   "source": [
    "![archtecture.jpg](attachment:archtecture.jpg)"
   ]
  },
  {
   "attachments": {
    "archtecture_total.jpg": {
     "image/jpeg": "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"
    }
   },
   "cell_type": "markdown",
   "id": "235fb9e4",
   "metadata": {},
   "source": [
    "![archtecture_total.jpg](attachment:archtecture_total.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "01af670f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Input,LSTM,Dense,BatchNormalization,Conv2D,Dropout,MaxPooling2D,Bidirectional,Reshape,Lambda\n",
    "from tensorflow.keras.models import Sequential,Model\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "import tensorflow.keras.backend as K\n",
    "from tensorflow.keras.layers import MaxPool2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "aedf0792",
   "metadata": {},
   "outputs": [],
   "source": [
    "# input with shape of height=32 and width=128 \n",
    "inputs = Input(shape=(32,128,1))\n",
    " \n",
    "# convolution layer with kernel size (3,3)\n",
    "conv_1 = Conv2D(64, (3,3), activation = 'relu', padding='same')(inputs)\n",
    "# poolig layer with kernel size (2,2)\n",
    "pool_1 = MaxPool2D(pool_size=(2, 2), strides=2)(conv_1)\n",
    " \n",
    "conv_2 = Conv2D(128, (3,3), activation = 'relu', padding='same')(pool_1)\n",
    "pool_2 = MaxPool2D(pool_size=(2, 2), strides=2)(conv_2)\n",
    " \n",
    "conv_3 = Conv2D(256, (3,3), activation = 'relu', padding='same')(pool_2)\n",
    " \n",
    "conv_4 = Conv2D(256, (3,3), activation = 'relu', padding='same')(conv_3)\n",
    "# poolig layer with kernel size (2,1)\n",
    "pool_4 = MaxPool2D(pool_size=(2, 1))(conv_4)\n",
    " \n",
    "conv_5 = Conv2D(512, (3,3), activation = 'relu', padding='same')(pool_4)\n",
    "# Batch normalization layer\n",
    "batch_norm_5 = BatchNormalization()(conv_5)\n",
    " \n",
    "conv_6 = Conv2D(512, (3,3), activation = 'relu', padding='same')(batch_norm_5)\n",
    "batch_norm_6 = BatchNormalization()(conv_6)\n",
    "pool_6 = MaxPool2D(pool_size=(2, 1))(batch_norm_6)\n",
    " \n",
    "conv_7 = Conv2D(512, (2,2), activation = 'relu')(pool_6)\n",
    " \n",
    "squeezed = Lambda(lambda x: K.squeeze(x, 1))(conv_7)\n",
    " \n",
    "# bidirectional LSTM layers with units=128\n",
    "blstm_1 = Bidirectional(LSTM(256, return_sequences=True, dropout = 0.2))(squeezed)\n",
    "blstm_2 = Bidirectional(LSTM(256, return_sequences=True, dropout = 0.2))(blstm_1)\n",
    " \n",
    "outputs = Dense(len(char_list)+1, activation = 'softmax')(blstm_2)\n",
    "\n",
    "# model to be used at test time\n",
    "model = Model(inputs, outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "921eab15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 32, 128, 1)]      0         \n",
      "                                                                 \n",
      " conv2d (Conv2D)             (None, 32, 128, 64)       640       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 16, 64, 64)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 16, 64, 128)       73856     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 8, 32, 128)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 8, 32, 256)        295168    \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 8, 32, 256)        590080    \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 4, 32, 256)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_4 (Conv2D)           (None, 4, 32, 512)        1180160   \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 4, 32, 512)       2048      \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " conv2d_5 (Conv2D)           (None, 4, 32, 512)        2359808   \n",
      "                                                                 \n",
      " batch_normalization_1 (Batc  (None, 4, 32, 512)       2048      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " max_pooling2d_3 (MaxPooling  (None, 2, 32, 512)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_6 (Conv2D)           (None, 1, 31, 512)        1049088   \n",
      "                                                                 \n",
      " lambda (Lambda)             (None, 31, 512)           0         \n",
      "                                                                 \n",
      " bidirectional (Bidirectiona  (None, 31, 512)          1574912   \n",
      " l)                                                              \n",
      "                                                                 \n",
      " bidirectional_1 (Bidirectio  (None, 31, 512)          1574912   \n",
      " nal)                                                            \n",
      "                                                                 \n",
      " dense (Dense)               (None, 31, 79)            40527     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 8,743,247\n",
      "Trainable params: 8,741,199\n",
      "Non-trainable params: 2,048\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f96c48bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights('best_model.hdf5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a9958bab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(876, 32, 128, 1)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "44f3f910",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = model.predict(valid_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ea8c5177",
   "metadata": {},
   "outputs": [],
   "source": [
    "decoded = K.ctc_decode(prediction, \n",
    "                       input_length=np.ones(prediction.shape[0]) * prediction.shape[1],\n",
    "                       greedy=True)[0][0]\n",
    "out = K.get_value(decoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2f434ad9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original_text =   A\n",
      "predicted text = A\n",
      "\n",
      "original_text =   Labour\n",
      "predicted text = Labour\n",
      "\n",
      "original_text =   of\n",
      "predicted text = of\n",
      "\n",
      "original_text =   down\n",
      "predicted text = down\n",
      "\n",
      "original_text =   Exchange\n",
      "predicted text = Crckauge\n",
      "\n",
      "original_text =   any\n",
      "predicted text = ay\n",
      "\n",
      "original_text =   a\n",
      "predicted text = a\n",
      "\n",
      "original_text =   has\n",
      "predicted text = ha\n",
      "\n",
      "original_text =   is\n",
      "predicted text = is\n",
      "\n",
      "original_text =   for\n",
      "predicted text = tor\n",
      "\n",
      "original_text =   support\n",
      "predicted text = support\n",
      "\n",
      "original_text =   to\n",
      "predicted text = to\n",
      "\n",
      "original_text =   will\n",
      "predicted text = wit\n",
      "\n",
      "original_text =   which\n",
      "predicted text = whored\n",
      "\n",
      "original_text =   now\n",
      "predicted text = now\n",
      "\n",
      "original_text =   of\n",
      "predicted text = of\n",
      "\n",
      "original_text =   take\n",
      "predicted text = take\n",
      "\n",
      "original_text =   Left-wing\n",
      "predicted text = rett-wing\n",
      "\n",
      "original_text =   likely\n",
      "predicted text = likely\n",
      "\n",
      "original_text =   Government\n",
      "predicted text = Government\n",
      "\n",
      "original_text =   should\n",
      "predicted text = schauld\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   should\n",
      "predicted text = should\n",
      "\n",
      "original_text =   prop\n",
      "predicted text = pror\n",
      "\n",
      "original_text =   likely\n",
      "predicted text = Rloly\n",
      "\n",
      "original_text =   ment\n",
      "predicted text = ment\n",
      "\n",
      "original_text =   should\n",
      "predicted text = should\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   should\n",
      "predicted text = should\n",
      "\n",
      "original_text =   prop\n",
      "predicted text = prep\n",
      "\n",
      "original_text =   13\n",
      "predicted text = 13s\n",
      "\n",
      "original_text =   Most\n",
      "predicted text = Most\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   has\n",
      "predicted text = has\n",
      "\n",
      "original_text =   two\n",
      "predicted text = huro\n",
      "\n",
      "original_text =   to\n",
      "predicted text = to\n",
      "\n",
      "original_text =   Welensky\n",
      "predicted text = Welensky\n",
      "\n",
      "original_text =   Labour\n",
      "predicted text = Labour\n",
      "\n",
      "original_text =   Labour\n",
      "predicted text = Labour\n",
      "\n",
      "original_text =   of\n",
      "predicted text = of\n",
      "\n",
      "original_text =   have\n",
      "predicted text = have\n",
      "\n",
      "original_text =   African\n",
      "predicted text = African\n",
      "\n",
      "original_text =   together\n",
      "predicted text = together\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   Peers\n",
      "predicted text = Pees\n",
      "\n",
      "original_text =   sentiment\n",
      "predicted text = sentiment\n",
      "\n",
      "original_text =   Lords\n",
      "predicted text = Hards\n",
      "\n",
      "original_text =   an\n",
      "predicted text = an\n",
      "\n",
      "original_text =   Nationalist\n",
      "predicted text = Notienalist\n",
      "\n",
      "original_text =   to\n",
      "predicted text = to\n",
      "\n",
      "original_text =   Federal\n",
      "predicted text = Federal\n",
      "\n",
      "original_text =   Independence\n",
      "predicted text = Independend\n",
      "\n",
      "original_text =   today\n",
      "predicted text = today\n",
      "\n",
      "original_text =   Roy\n",
      "predicted text = Roy\n",
      "\n",
      "original_text =   in\n",
      "predicted text = in\n",
      "\n",
      "original_text =   Iain\n",
      "predicted text = lain\n",
      "\n",
      "original_text =   .\n",
      "predicted text = .\n",
      "\n",
      "original_text =   (\n",
      "predicted text = ,\n",
      "\n",
      "original_text =   Congress\n",
      "predicted text = Congress\n",
      "\n",
      "original_text =   discuss\n",
      "predicted text = discuss\n",
      "\n",
      "original_text =   violently\n",
      "predicted text = sikently\n",
      "\n",
      "original_text =   Rhodesia\n",
      "predicted text = Rhotesia\n",
      "\n",
      "original_text =   ,\n",
      "predicted text = ,\n",
      "\n",
      "original_text =   insisting\n",
      "predicted text = incitiry\n",
      "\n",
      "original_text =   Federal\n",
      "predicted text = Federal\n",
      "\n",
      "original_text =   future\n",
      "predicted text = future\n",
      "\n",
      "original_text =   want\n",
      "predicted text = vant\n",
      "\n",
      "original_text =   gives\n",
      "predicted text = gives\n",
      "\n",
      "original_text =   .\n",
      "predicted text = .\n",
      "\n",
      "original_text =   his\n",
      "predicted text = his\n",
      "\n",
      "original_text =   at\n",
      "predicted text = at\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   to\n",
      "predicted text = to\n",
      "\n",
      "original_text =   Roy's\n",
      "predicted text = Roy's\n",
      "\n",
      "original_text =   chief\n",
      "predicted text = chief\n",
      "\n",
      "original_text =   Chequers\n",
      "predicted text = Cheanors\n",
      "\n",
      "original_text =   London\n",
      "predicted text = condon\n",
      "\n",
      "original_text =   last\n",
      "predicted text = last\n",
      "\n",
      "original_text =   do\n",
      "predicted text = di\n",
      "\n",
      "original_text =   and\n",
      "predicted text = and\n",
      "\n",
      "original_text =   be\n",
      "predicted text = be\n",
      "\n",
      "original_text =   Mr.\n",
      "predicted text = Mr.\n",
      "\n",
      "original_text =   his\n",
      "predicted text = his\n",
      "\n",
      "original_text =   went\n",
      "predicted text = went\n",
      "\n",
      "original_text =   crisis\n",
      "predicted text = criis\n",
      "\n",
      "original_text =   his\n",
      "predicted text = his\n",
      "\n",
      "original_text =   These\n",
      "predicted text = These\n",
      "\n",
      "original_text =   they\n",
      "predicted text = they\n",
      "\n",
      "original_text =   .\n",
      "predicted text = .\n",
      "\n",
      "original_text =   a\n",
      "predicted text = a\n",
      "\n",
      "original_text =   with\n",
      "predicted text = with\n",
      "\n",
      "original_text =   had\n",
      "predicted text = had\n",
      "\n",
      "original_text =   plans\n",
      "predicted text = plans\n",
      "\n",
      "original_text =   do\n",
      "predicted text = do\n",
      "\n",
      "original_text =   seeking\n",
      "predicted text = seeking\n",
      "\n",
      "original_text =   conference\n",
      "predicted text = conference\n",
      "\n",
      "original_text =   House\n",
      "predicted text = House\n",
      "\n",
      "original_text =   conference\n",
      "predicted text = conference\n",
      "\n",
      "original_text =   up\n",
      "predicted text = up\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   give\n",
      "predicted text = give\n",
      "\n",
      "original_text =   .\n",
      "predicted text = .\n",
      "\n",
      "original_text =   will\n",
      "predicted text = will\n",
      "\n",
      "original_text =   of\n",
      "predicted text = of\n",
      "\n",
      "original_text =   ,\n",
      "predicted text = ,\n",
      "\n",
      "original_text =   been\n",
      "predicted text = been\n",
      "\n",
      "original_text =   Rhodesia\n",
      "predicted text = Rhodesra\n",
      "\n",
      "original_text =   main\n",
      "predicted text = man\n",
      "\n",
      "original_text =   Dominion\n",
      "predicted text = Cominion\n",
      "\n",
      "original_text =   Prime\n",
      "predicted text = Prime\n",
      "\n",
      "original_text =   Chequers\n",
      "predicted text = Cleguers\n",
      "\n",
      "original_text =   MR.\n",
      "predicted text = Mr.\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   on\n",
      "predicted text = on\n",
      "\n",
      "original_text =   London\n",
      "predicted text = Landon\n",
      "\n",
      "original_text =   -\n",
      "predicted text = -\n",
      "\n",
      "original_text =   But\n",
      "predicted text = But\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   week-end\n",
      "predicted text = week-end\n",
      "\n",
      "original_text =   ,\n",
      "predicted text = ,\n",
      "\n",
      "original_text =   night\n",
      "predicted text = maght\n",
      "\n",
      "original_text =   Rhodesia's\n",
      "predicted text = Rhodeia's\n",
      "\n",
      "original_text =   of\n",
      "predicted text = of\n",
      "\n",
      "original_text =   African\n",
      "predicted text = Nfrnean\n",
      "\n",
      "original_text =   talks\n",
      "predicted text = talks\n",
      "\n",
      "original_text =   of\n",
      "predicted text = of\n",
      "\n",
      "original_text =   I\n",
      "predicted text = ,\n",
      "\n",
      "original_text =   said\n",
      "predicted text = said\n",
      "\n",
      "original_text =   .\n",
      "predicted text = .\n",
      "\n",
      "original_text =   Sir\n",
      "predicted text = Sir\n",
      "\n",
      "original_text =   talks\n",
      "predicted text = talks\n",
      "\n",
      "original_text =   Northern\n",
      "predicted text = Northern\n",
      "\n",
      "original_text =   Macleod\n",
      "predicted text = Macleod\n",
      "\n",
      "original_text =   secret\n",
      "predicted text = secret\n",
      "\n",
      "original_text =   to\n",
      "predicted text = to\n",
      "\n",
      "original_text =   remained\n",
      "predicted text = remained\n",
      "\n",
      "original_text =   Labour's\n",
      "predicted text = Labour's\n",
      "\n",
      "original_text =   to\n",
      "predicted text = to\n",
      "\n",
      "original_text =   round\n",
      "predicted text = round\n",
      "\n",
      "original_text =   of\n",
      "predicted text = of\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   \"\n",
      "predicted text = \"\n",
      "\n",
      "original_text =   He\n",
      "predicted text = He\n",
      "\n",
      "original_text =   said\n",
      "predicted text = said\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   .\n",
      "predicted text = .\n",
      "\n",
      "original_text =   to\n",
      "predicted text = to\n",
      "\n",
      "original_text =   sent\n",
      "predicted text = sent\n",
      "\n",
      "original_text =   Macmillan\n",
      "predicted text = Macmillan\n",
      "\n",
      "original_text =   Mr.\n",
      "predicted text = Mr.\n",
      "\n",
      "original_text =   week-end\n",
      "predicted text = weele-and\n",
      "\n",
      "original_text =   \"\n",
      "predicted text = \"\n",
      "\n",
      "original_text =   Roy's\n",
      "predicted text = moy'os\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   because\n",
      "predicted text = beeaase\n",
      "\n",
      "original_text =   were\n",
      "predicted text = ware\n",
      "\n",
      "original_text =   by\n",
      "predicted text = by\n",
      "\n",
      "original_text =   talks\n",
      "predicted text = sulks\n",
      "\n",
      "original_text =   today\n",
      "predicted text = dolany\n",
      "\n",
      "original_text =   Minister\n",
      "predicted text = Miniter\n",
      "\n",
      "original_text =   will\n",
      "predicted text = will\n",
      "\n",
      "original_text =   Government\n",
      "predicted text = Governmment\n",
      "\n",
      "original_text =   Lancaster\n",
      "predicted text = Lancaster\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   It\n",
      "predicted text = It\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   Weaver\n",
      "predicted text = Weover\n",
      "\n",
      "original_text =   be\n",
      "predicted text = be\n",
      "\n",
      "original_text =   and\n",
      "predicted text = and\n",
      "\n",
      "original_text =   will\n",
      "predicted text = will\n",
      "\n",
      "original_text =   of\n",
      "predicted text = of\n",
      "\n",
      "original_text =   aroused\n",
      "predicted text = oroeved\n",
      "\n",
      "original_text =   South\n",
      "predicted text = Bacts\n",
      "\n",
      "original_text =   York\n",
      "predicted text = bak\n",
      "\n",
      "original_text =   there\n",
      "predicted text = there\n",
      "\n",
      "original_text =   projects\n",
      "predicted text = pists\n",
      "\n",
      "original_text =   opposition\n",
      "predicted text = oppoition\n",
      "\n",
      "original_text =   Washington\n",
      "predicted text = Waskigthon\n",
      "\n",
      "original_text =   The\n",
      "predicted text = The\n",
      "\n",
      "original_text =   Southern\n",
      "predicted text = lautha\n",
      "\n",
      "original_text =   today\n",
      "predicted text = toalay\n",
      "\n",
      "original_text =   Robertson\n",
      "predicted text = Noberlan\n",
      "\n",
      "original_text =   Kennedy\n",
      "predicted text = Keneedy\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   current\n",
      "predicted text = ourrent\n",
      "\n",
      "original_text =   .\n",
      "predicted text = .\n",
      "\n",
      "original_text =   another\n",
      "predicted text = another\n",
      "\n",
      "original_text =   met\n",
      "predicted text = met\n",
      "\n",
      "original_text =   Mr.\n",
      "predicted text = Mr.\n",
      "\n",
      "original_text =   television\n",
      "predicted text = telesioion\n",
      "\n",
      "original_text =   Weaver\n",
      "predicted text = Weaver\n",
      "\n",
      "original_text =   Committee\n",
      "predicted text = camittee\n",
      "\n",
      "original_text =   Willis\n",
      "predicted text = Willis\n",
      "\n",
      "original_text =   he\n",
      "predicted text = he\n",
      "\n",
      "original_text =   had\n",
      "predicted text = had\n",
      "\n",
      "original_text =   by\n",
      "predicted text = by\n",
      "\n",
      "original_text =   to\n",
      "predicted text = to\n",
      "\n",
      "original_text =   perform\n",
      "predicted text = pofon\n",
      "\n",
      "original_text =   Robertson's\n",
      "predicted text = Raterton's\n",
      "\n",
      "original_text =   can\n",
      "predicted text = on\n",
      "\n",
      "original_text =   He\n",
      "predicted text = He\n",
      "\n",
      "original_text =   black-\n",
      "predicted text = black-\n",
      "\n",
      "original_text =   a\n",
      "predicted text = a\n",
      "\n",
      "original_text =   Investigation\n",
      "predicted text = hnrestigation\n",
      "\n",
      "original_text =   would\n",
      "predicted text = would\n",
      "\n",
      "original_text =   Senator\n",
      "predicted text = Senator\n",
      "\n",
      "original_text =   it\n",
      "predicted text = it\n",
      "\n",
      "original_text =   to\n",
      "predicted text = to\n",
      "\n",
      "original_text =   reported\n",
      "predicted text = reppoded\n",
      "\n",
      "original_text =   \"\n",
      "predicted text = \"\n",
      "\n",
      "original_text =   committee\n",
      "predicted text = cmmittee\n",
      "\n",
      "original_text =   be\n",
      "predicted text = he\n",
      "\n",
      "original_text =   ready\n",
      "predicted text = sesly\n",
      "\n",
      "original_text =   help\n",
      "predicted text = bhfy\n",
      "\n",
      "original_text =   in\n",
      "predicted text = is\n",
      "\n",
      "original_text =   was\n",
      "predicted text = mas\n",
      "\n",
      "original_text =   He\n",
      "predicted text = Se\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   get\n",
      "predicted text = get\n",
      "\n",
      "original_text =   balance\n",
      "predicted text = balace\n",
      "\n",
      "original_text =   yesterday\n",
      "predicted text = yesterday\n",
      "\n",
      "original_text =   enough\n",
      "predicted text = anough\n",
      "\n",
      "original_text =   to\n",
      "predicted text = to\n",
      "\n",
      "original_text =   mention\n",
      "predicted text = meution\n",
      "\n",
      "original_text =   Chancellor\n",
      "predicted text = Chancellor\n",
      "\n",
      "original_text =   West\n",
      "predicted text = Wa\n",
      "\n",
      "original_text =   position\n",
      "predicted text = position\n",
      "\n",
      "original_text =   -\n",
      "predicted text = -\n",
      "\n",
      "original_text =   indicated\n",
      "predicted text = inlinted\n",
      "\n",
      "original_text =   pay\n",
      "predicted text = pay\n",
      "\n",
      "original_text =   Dr.\n",
      "predicted text = Mr.\n",
      "\n",
      "original_text =   said\n",
      "predicted text = said\n",
      "\n",
      "original_text =   discuss\n",
      "predicted text = disos\n",
      "\n",
      "original_text =   Foreign\n",
      "predicted text = Forgin\n",
      "\n",
      "original_text =   .\n",
      "predicted text = .\n",
      "\n",
      "original_text =   .\n",
      "predicted text = .\n",
      "\n",
      "original_text =   money\n",
      "predicted text = mon\n",
      "\n",
      "original_text =   might\n",
      "predicted text = night\n",
      "\n",
      "original_text =   problem\n",
      "predicted text = problm\n",
      "\n",
      "original_text =   ,\n",
      "predicted text = ,\n",
      "\n",
      "original_text =   big\n",
      "predicted text = big\n",
      "\n",
      "original_text =   payment\n",
      "predicted text = sayment\n",
      "\n",
      "original_text =   officials\n",
      "predicted text = officials\n",
      "\n",
      "original_text =   America\n",
      "predicted text = America\n",
      "\n",
      "original_text =   higher\n",
      "predicted text = miphar\n",
      "\n",
      "original_text =   .\n",
      "predicted text = .\n",
      "\n",
      "original_text =   due\n",
      "predicted text = due\n",
      "\n",
      "original_text =   Germany's\n",
      "predicted text = Eemoreny's\n",
      "\n",
      "original_text =   210million\n",
      "predicted text = 210millian\n",
      "\n",
      "original_text =   And\n",
      "predicted text = to\n",
      "\n",
      "original_text =   cannot\n",
      "predicted text = cannot\n",
      "\n",
      "original_text =   .\n",
      "predicted text = .\n",
      "\n",
      "original_text =   and\n",
      "predicted text = and\n",
      "\n",
      "original_text =   present\n",
      "predicted text = present\n",
      "\n",
      "original_text =   Eisenhower\n",
      "predicted text = Eisentower\n",
      "\n",
      "original_text =   towards\n",
      "predicted text = towards\n",
      "\n",
      "original_text =   These\n",
      "predicted text = These\n",
      "\n",
      "original_text =   reserves\n",
      "predicted text = resoves\n",
      "\n",
      "original_text =   of\n",
      "predicted text = of\n",
      "\n",
      "original_text =   painful\n",
      "predicted text = painfal\n",
      "\n",
      "original_text =   now\n",
      "predicted text = now\n",
      "\n",
      "original_text =   do\n",
      "predicted text = do\n",
      "\n",
      "original_text =   upsetting\n",
      "predicted text = upsetting\n",
      "\n",
      "original_text =   a\n",
      "predicted text = a\n",
      "\n",
      "original_text =   answer\n",
      "predicted text = aumer\n",
      "\n",
      "original_text =   Kennedy's\n",
      "predicted text = hessdin\n",
      "\n",
      "original_text =   West\n",
      "predicted text = Whet\n",
      "\n",
      "original_text =   .\n",
      "predicted text = .\n",
      "\n",
      "original_text =   Germany\n",
      "predicted text = Gerenny\n",
      "\n",
      "original_text =   Joyce\n",
      "predicted text = fave\n",
      "\n",
      "original_text =   con-\n",
      "predicted text = Conn-\n",
      "\n",
      "original_text =   lagging\n",
      "predicted text = leging\n",
      "\n",
      "original_text =   was\n",
      "predicted text = was\n",
      "\n",
      "original_text =   with\n",
      "predicted text = with\n",
      "\n",
      "original_text =   is\n",
      "predicted text = is\n",
      "\n",
      "original_text =   President\n",
      "predicted text = President\n",
      "\n",
      "original_text =   not\n",
      "predicted text = not\n",
      "\n",
      "original_text =   power\n",
      "predicted text = power\n",
      "\n",
      "original_text =   military\n",
      "predicted text = military\n",
      "\n",
      "original_text =   20\n",
      "predicted text = 20\n",
      "\n",
      "original_text =   from\n",
      "predicted text = from\n",
      "\n",
      "original_text =   claimed\n",
      "predicted text = claimed\n",
      "\n",
      "original_text =   lag\n",
      "predicted text = lag\n",
      "\n",
      "original_text =   his\n",
      "predicted text = his\n",
      "\n",
      "original_text =   George\n",
      "predicted text = George\n",
      "\n",
      "original_text =   personality\n",
      "predicted text = personality\n",
      "\n",
      "original_text =   ,\n",
      "predicted text = ,\n",
      "\n",
      "original_text =   Mr.\n",
      "predicted text = Mr.\n",
      "\n",
      "original_text =   a\n",
      "predicted text = a\n",
      "\n",
      "original_text =   MPs\n",
      "predicted text = MPs\n",
      "\n",
      "original_text =   issue\n",
      "predicted text = ie\n",
      "\n",
      "original_text =   Brown\n",
      "predicted text = Brown\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   outwardly\n",
      "predicted text = outwardly\n",
      "\n",
      "original_text =   ended\n",
      "predicted text = anded\n",
      "\n",
      "original_text =   dealt\n",
      "predicted text = dealt\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   come\n",
      "predicted text = come\n",
      "\n",
      "original_text =   in\n",
      "predicted text = in\n",
      "\n",
      "original_text =   to\n",
      "predicted text = to\n",
      "\n",
      "original_text =   Government\n",
      "predicted text = Government\n",
      "\n",
      "original_text =   heavy\n",
      "predicted text = hony\n",
      "\n",
      "original_text =   Mr.\n",
      "predicted text = Mr.\n",
      "\n",
      "original_text =   .\n",
      "predicted text = .\n",
      "\n",
      "original_text =   of\n",
      "predicted text = of\n",
      "\n",
      "original_text =   not\n",
      "predicted text = not\n",
      "\n",
      "original_text =   answer\n",
      "predicted text = auswer\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   placing\n",
      "predicted text = placing\n",
      "\n",
      "original_text =   .\n",
      "predicted text = .\n",
      "\n",
      "original_text =   monstrous\n",
      "predicted text = monstrons\n",
      "\n",
      "original_text =   ranks\n",
      "predicted text = rauk\n",
      "\n",
      "original_text =   and\n",
      "predicted text = and\n",
      "\n",
      "original_text =   this\n",
      "predicted text = this\n",
      "\n",
      "original_text =   medicine\n",
      "predicted text = mediee\n",
      "\n",
      "original_text =   .\n",
      "predicted text = .\n",
      "\n",
      "original_text =   horrified\n",
      "predicted text = horiified\n",
      "\n",
      "original_text =   whole\n",
      "predicted text = whole\n",
      "\n",
      "original_text =   service\n",
      "predicted text = serviee\n",
      "\n",
      "original_text =   lifetime\n",
      "predicted text = Lifetine\n",
      "\n",
      "original_text =   considered\n",
      "predicted text = convidwed\n",
      "\n",
      "original_text =   which\n",
      "predicted text = which\n",
      "\n",
      "original_text =   it\n",
      "predicted text = it\n",
      "\n",
      "original_text =   from\n",
      "predicted text = from\n",
      "\n",
      "original_text =   cost\n",
      "predicted text = aost\n",
      "\n",
      "original_text =   healing\n",
      "predicted text = Keaking\n",
      "\n",
      "original_text =   supporting\n",
      "predicted text = suprosting\n",
      "\n",
      "original_text =   .\n",
      "predicted text = .\n",
      "\n",
      "original_text =   dealing\n",
      "predicted text = deaking\n",
      "\n",
      "original_text =   to\n",
      "predicted text = to\n",
      "\n",
      "original_text =   surveyor\n",
      "predicted text = swurvlyer\n",
      "\n",
      "original_text =   fiddling\n",
      "predicted text = fiaadting\n",
      "\n",
      "original_text =   a\n",
      "predicted text = a\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   sick\n",
      "predicted text = sicte\n",
      "\n",
      "original_text =   \"\n",
      "predicted text = \"\n",
      "\n",
      "original_text =   can\n",
      "predicted text = can\n",
      "\n",
      "original_text =   medical\n",
      "predicted text = medical\n",
      "\n",
      "original_text =   of\n",
      "predicted text = of\n",
      "\n",
      "original_text =   ,\n",
      "predicted text = ,\n",
      "\n",
      "original_text =   it\n",
      "predicted text = it\n",
      "\n",
      "original_text =   by\n",
      "predicted text = by\n",
      "\n",
      "original_text =   jackals\n",
      "predicted text = jackals\n",
      "\n",
      "original_text =   .\n",
      "predicted text = .\n",
      "\n",
      "original_text =   services\n",
      "predicted text = rervices\n",
      "\n",
      "original_text =   Labour\n",
      "predicted text = Lator\n",
      "\n",
      "original_text =   absolutely\n",
      "predicted text = shwatitly\n",
      "\n",
      "original_text =   nor\n",
      "predicted text = nas\n",
      "\n",
      "original_text =   was\n",
      "predicted text = was\n",
      "\n",
      "original_text =   and\n",
      "predicted text = and\n",
      "\n",
      "original_text =   benches\n",
      "predicted text = hencher\n",
      "\n",
      "original_text =   strain\n",
      "predicted text = shrain\n",
      "\n",
      "original_text =   their\n",
      "predicted text = their\n",
      "\n",
      "original_text =   different\n",
      "predicted text = different\n",
      "\n",
      "original_text =   ambulance\n",
      "predicted text = ambulance\n",
      "\n",
      "original_text =   not\n",
      "predicted text = not\n",
      "\n",
      "original_text =   Answering\n",
      "predicted text = Answering\n",
      "\n",
      "original_text =   ceiling\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted text = caiting\n",
      "\n",
      "original_text =   reminded\n",
      "predicted text = reminded\n",
      "\n",
      "original_text =   because\n",
      "predicted text = Gecouse\n",
      "\n",
      "original_text =   in\n",
      "predicted text = in\n",
      "\n",
      "original_text =   expenditure\n",
      "predicted text = ccpanditue\n",
      "\n",
      "original_text =   cent.\n",
      "predicted text = cent\n",
      "\n",
      "original_text =   unsmiling\n",
      "predicted text = unomting\n",
      "\n",
      "original_text =   it\n",
      "predicted text = it\n",
      "\n",
      "original_text =   action\n",
      "predicted text = action\n",
      "\n",
      "original_text =   risen\n",
      "predicted text = risen\n",
      "\n",
      "original_text =   eight\n",
      "predicted text = cight\n",
      "\n",
      "original_text =   worse\n",
      "predicted text = wore\n",
      "\n",
      "original_text =   to\n",
      "predicted text = to\n",
      "\n",
      "original_text =   of\n",
      "predicted text = of\n",
      "\n",
      "original_text =   to\n",
      "predicted text = to\n",
      "\n",
      "original_text =   directly\n",
      "predicted text = airectly\n",
      "\n",
      "original_text =   instead\n",
      "predicted text = instedd\n",
      "\n",
      "original_text =   should\n",
      "predicted text = should\n",
      "\n",
      "original_text =   #\n",
      "predicted text = to\n",
      "\n",
      "original_text =   on\n",
      "predicted text = on\n",
      "\n",
      "original_text =   .\n",
      "predicted text = .\n",
      "\n",
      "original_text =   cost\n",
      "predicted text = cost\n",
      "\n",
      "original_text =   health\n",
      "predicted text = health\n",
      "\n",
      "original_text =   scheme\n",
      "predicted text = scheme\n",
      "\n",
      "original_text =   be\n",
      "predicted text = be\n",
      "\n",
      "original_text =   to\n",
      "predicted text = to\n",
      "\n",
      "original_text =   people\n",
      "predicted text = people\n",
      "\n",
      "original_text =   Mr.\n",
      "predicted text = Mr.\n",
      "\n",
      "original_text =   full\n",
      "predicted text = full\n",
      "\n",
      "original_text =   industry\n",
      "predicted text = industy\n",
      "\n",
      "original_text =   to\n",
      "predicted text = to\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   seemed\n",
      "predicted text = sllmed\n",
      "\n",
      "original_text =   Brown\n",
      "predicted text = Bromn\n",
      "\n",
      "original_text =   -\n",
      "predicted text = -\n",
      "\n",
      "original_text =   indeed\n",
      "predicted text = ndeed\n",
      "\n",
      "original_text =   of\n",
      "predicted text = of\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   per\n",
      "predicted text = per\n",
      "\n",
      "original_text =   for\n",
      "predicted text = for\n",
      "\n",
      "original_text =   on\n",
      "predicted text = on\n",
      "\n",
      "original_text =   speech\n",
      "predicted text = speech\n",
      "\n",
      "original_text =   of\n",
      "predicted text = of\n",
      "\n",
      "original_text =   three\n",
      "predicted text = thee\n",
      "\n",
      "original_text =   in\n",
      "predicted text = in\n",
      "\n",
      "original_text =   financing\n",
      "predicted text = fimancing\n",
      "\n",
      "original_text =   undermining\n",
      "predicted text = nndnsining\n",
      "\n",
      "original_text =   economic\n",
      "predicted text = cvonnin\n",
      "\n",
      "original_text =   that\n",
      "predicted text = that\n",
      "\n",
      "original_text =   receiving\n",
      "predicted text = neceiving\n",
      "\n",
      "original_text =   without\n",
      "predicted text = withont\n",
      "\n",
      "original_text =   in\n",
      "predicted text = in\n",
      "\n",
      "original_text =   financing\n",
      "predicted text = financing\n",
      "\n",
      "original_text =   undermining\n",
      "predicted text = undomining\n",
      "\n",
      "original_text =   economic\n",
      "predicted text = economie\n",
      "\n",
      "original_text =   that\n",
      "predicted text = that\n",
      "\n",
      "original_text =   receiving\n",
      "predicted text = recliving\n",
      "\n",
      "original_text =   without\n",
      "predicted text = withant\n",
      "\n",
      "original_text =   in\n",
      "predicted text = in\n",
      "\n",
      "original_text =   financing\n",
      "predicted text = financing\n",
      "\n",
      "original_text =   undermining\n",
      "predicted text = undemining\n",
      "\n",
      "original_text =   economic\n",
      "predicted text = ecohomni\n",
      "\n",
      "original_text =   all\n",
      "predicted text = dll\n",
      "\n",
      "original_text =   regular\n",
      "predicted text = regular\n",
      "\n",
      "original_text =   strong\n",
      "predicted text = strong\n",
      "\n",
      "original_text =   joining\n",
      "predicted text = Piring\n",
      "\n",
      "original_text =   to\n",
      "predicted text = to\n",
      "\n",
      "original_text =   way\n",
      "predicted text = wey\n",
      "\n",
      "original_text =   Britain's\n",
      "predicted text = Britain's\n",
      "\n",
      "original_text =   gesture\n",
      "predicted text = Yestuere\n",
      "\n",
      "original_text =   So\n",
      "predicted text = So0\n",
      "\n",
      "original_text =   to\n",
      "predicted text = to\n",
      "\n",
      "original_text =   a\n",
      "predicted text = a\n",
      "\n",
      "original_text =   Heath\n",
      "predicted text = Heath\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   same\n",
      "predicted text = Same\n",
      "\n",
      "original_text =   told\n",
      "predicted text = told\n",
      "\n",
      "original_text =   wish\n",
      "predicted text = wish\n",
      "\n",
      "original_text =   of\n",
      "predicted text = of\n",
      "\n",
      "original_text =   will\n",
      "predicted text = will\n",
      "\n",
      "original_text =   on\n",
      "predicted text = on\n",
      "\n",
      "original_text =   draws\n",
      "predicted text = drams\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   experienced\n",
      "predicted text = experienced\n",
      "\n",
      "original_text =   has\n",
      "predicted text = has\n",
      "\n",
      "original_text =   his\n",
      "predicted text = wis\n",
      "\n",
      "original_text =   in\n",
      "predicted text = in\n",
      "\n",
      "original_text =   tary\n",
      "predicted text = tarmy\n",
      "\n",
      "original_text =   Fisheries\n",
      "predicted text = Tisheines\n",
      "\n",
      "original_text =   Barclay\n",
      "predicted text = Borday\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   Office\n",
      "predicted text = Office\n",
      "\n",
      "original_text =   he\n",
      "predicted text = he\n",
      "\n",
      "original_text =   Commission\n",
      "predicted text = Gommisio\n",
      "\n",
      "original_text =   held\n",
      "predicted text = hald\n",
      "\n",
      "original_text =   a\n",
      "predicted text = a\n",
      "\n",
      "original_text =   French\n",
      "predicted text = Prenel\n",
      "\n",
      "original_text =   Lloyd\n",
      "predicted text = dlagd\n",
      "\n",
      "original_text =   seems\n",
      "predicted text = seem\n",
      "\n",
      "original_text =   it\n",
      "predicted text = it\n",
      "\n",
      "original_text =   NATO\n",
      "predicted text = N4to\n",
      "\n",
      "original_text =   demonstrations\n",
      "predicted text = domnstrntions\n",
      "\n",
      "original_text =   monwealth\n",
      "predicted text = monealth\n",
      "\n",
      "original_text =   in\n",
      "predicted text = i\n",
      "\n",
      "original_text =   Lloyd\n",
      "predicted text = dlrgd\n",
      "\n",
      "original_text =   ,\n",
      "predicted text = ,\n",
      "\n",
      "original_text =   Stores\n",
      "predicted text = Ptmes\n",
      "\n",
      "original_text =   been\n",
      "predicted text = been\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   of\n",
      "predicted text = of\n",
      "\n",
      "original_text =   compulsory\n",
      "predicted text = compulsany\n",
      "\n",
      "original_text =   .\n",
      "predicted text = .\n",
      "\n",
      "original_text =   storm\n",
      "predicted text = stom\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   on\n",
      "predicted text = on\n",
      "\n",
      "original_text =   know\n",
      "predicted text = h\n",
      "\n",
      "original_text =   his\n",
      "predicted text = his\n",
      "\n",
      "original_text =   strong\n",
      "predicted text = strong\n",
      "\n",
      "original_text =   and\n",
      "predicted text = and\n",
      "\n",
      "original_text =   (\n",
      "predicted text = (\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   has\n",
      "predicted text = has\n",
      "\n",
      "original_text =   ruthlessly\n",
      "predicted text = rathlersl\n",
      "\n",
      "original_text =   a\n",
      "predicted text = a\n",
      "\n",
      "original_text =   powerful\n",
      "predicted text = porerful\n",
      "\n",
      "original_text =   \"\n",
      "predicted text = \"\n",
      "\n",
      "original_text =   In\n",
      "predicted text = in\n",
      "\n",
      "original_text =   essential\n",
      "predicted text = eenticl\n",
      "\n",
      "original_text =   an\n",
      "predicted text = en\n",
      "\n",
      "original_text =   a\n",
      "predicted text = a\n",
      "\n",
      "original_text =   measures\n",
      "predicted text = meslas\n",
      "\n",
      "original_text =   how\n",
      "predicted text = haw\n",
      "\n",
      "original_text =   Accra\n",
      "predicted text = teere\n",
      "\n",
      "original_text =   ,\n",
      "predicted text = ,\n",
      "\n",
      "original_text =   demonstrators\n",
      "predicted text = demorsteators\n",
      "\n",
      "original_text =   If\n",
      "predicted text = i\n",
      "\n",
      "original_text =   he\n",
      "predicted text = he\n",
      "\n",
      "original_text =   '\n",
      "predicted text = ,\n",
      "\n",
      "original_text =   and\n",
      "predicted text = and\n",
      "\n",
      "original_text =   .\n",
      "predicted text = .\n",
      "\n",
      "original_text =   have\n",
      "predicted text = havs\n",
      "\n",
      "original_text =   of\n",
      "predicted text = of\n",
      "\n",
      "original_text =   .\n",
      "predicted text = .\n",
      "\n",
      "original_text =   this\n",
      "predicted text = this\n",
      "\n",
      "original_text =   .\n",
      "predicted text = .\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   a\n",
      "predicted text = a\n",
      "\n",
      "original_text =   on\n",
      "predicted text = on\n",
      "\n",
      "original_text =   members\n",
      "predicted text = memberss\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   put\n",
      "predicted text = put\n",
      "\n",
      "original_text =   It\n",
      "predicted text = It\n",
      "\n",
      "original_text =   pretext\n",
      "predicted text = prebesl\n",
      "\n",
      "original_text =   \"\n",
      "predicted text = \"\n",
      "\n",
      "original_text =   connection\n",
      "predicted text = comnection\n",
      "\n",
      "original_text =   that\n",
      "predicted text = that\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   Lady\n",
      "predicted text = blby\n",
      "\n",
      "original_text =   why\n",
      "predicted text = wshy\n",
      "\n",
      "original_text =   peace\n",
      "predicted text = peace\n",
      "\n",
      "original_text =   The\n",
      "predicted text = the\n",
      "\n",
      "original_text =   such\n",
      "predicted text = snch\n",
      "\n",
      "original_text =   was\n",
      "predicted text = was\n",
      "\n",
      "original_text =   ,\n",
      "predicted text = ,\n",
      "\n",
      "original_text =   is\n",
      "predicted text = is\n",
      "\n",
      "original_text =   That\n",
      "predicted text = Thet\n",
      "\n",
      "original_text =   he\n",
      "predicted text = he\n",
      "\n",
      "original_text =   prison\n",
      "predicted text = prison\n",
      "\n",
      "original_text =   to\n",
      "predicted text = to\n",
      "\n",
      "original_text =   conference\n",
      "predicted text = conference\n",
      "\n",
      "original_text =   \"\n",
      "predicted text = \"\n",
      "\n",
      "original_text =   a\n",
      "predicted text = a\n",
      "\n",
      "original_text =   planned\n",
      "predicted text = phonset\n",
      "\n",
      "original_text =   today\n",
      "predicted text = olag\n",
      "\n",
      "original_text =   Mr.\n",
      "predicted text = Mr.\n",
      "\n",
      "original_text =   was\n",
      "predicted text = Was\n",
      "\n",
      "original_text =   ,\n",
      "predicted text = ,\n",
      "\n",
      "original_text =   outlawed\n",
      "predicted text = outlowed\n",
      "\n",
      "original_text =   supporters\n",
      "predicted text = supposters\n",
      "\n",
      "original_text =   the\n",
      "predicted text = te\n",
      "\n",
      "original_text =   which\n",
      "predicted text = which\n",
      "\n",
      "original_text =   since\n",
      "predicted text = snoe\n",
      "\n",
      "original_text =   the\n",
      "predicted text = The\n",
      "\n",
      "original_text =   had\n",
      "predicted text = had\n",
      "\n",
      "original_text =   people\n",
      "predicted text = fesple\n",
      "\n",
      "original_text =   were\n",
      "predicted text = were\n",
      "\n",
      "original_text =   \"\n",
      "predicted text = \"\n",
      "\n",
      "original_text =   statements\n",
      "predicted text = fulemonls\n",
      "\n",
      "original_text =   am\n",
      "predicted text = an\n",
      "\n",
      "original_text =   good\n",
      "predicted text = ged\n",
      "\n",
      "original_text =   that\n",
      "predicted text = that\n",
      "\n",
      "original_text =   done\n",
      "predicted text = done\n",
      "\n",
      "original_text =   he\n",
      "predicted text = he\n",
      "\n",
      "original_text =   Rhodesia\n",
      "predicted text = Rhodesia\n",
      "\n",
      "original_text =   its\n",
      "predicted text = its\n",
      "\n",
      "original_text =   in\n",
      "predicted text = in\n",
      "\n",
      "original_text =   JOHN\n",
      "predicted text = DoHN\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   yesterday\n",
      "predicted text = yestertay\n",
      "\n",
      "original_text =   ,\n",
      "predicted text = ,\n",
      "\n",
      "original_text =   an\n",
      "predicted text = an\n",
      "\n",
      "original_text =   expected\n",
      "predicted text = ekpected\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   an\n",
      "predicted text = an\n",
      "\n",
      "original_text =   any\n",
      "predicted text = any\n",
      "\n",
      "original_text =   with\n",
      "predicted text = with\n",
      "\n",
      "original_text =   and\n",
      "predicted text = and\n",
      "\n",
      "original_text =   Africans\n",
      "predicted text = Africans\n",
      "\n",
      "original_text =   A\n",
      "predicted text = Io\n",
      "\n",
      "original_text =   Freddie\n",
      "predicted text = Freddie\n",
      "\n",
      "original_text =   at\n",
      "predicted text = at\n",
      "\n",
      "original_text =   yesterday\n",
      "predicted text = yesserday\n",
      "\n",
      "original_text =   heads\n",
      "predicted text = head\n",
      "\n",
      "original_text =   a\n",
      "predicted text = a\n",
      "\n",
      "original_text =   and\n",
      "predicted text = and\n",
      "\n",
      "original_text =   arrived\n",
      "predicted text = arived\n",
      "\n",
      "original_text =   before\n",
      "predicted text = before\n",
      "\n",
      "original_text =   question\n",
      "predicted text = guestion\n",
      "\n",
      "original_text =   thorniest\n",
      "predicted text = thormiest\n",
      "\n",
      "original_text =   relationships\n",
      "predicted text = relationshinps\n",
      "\n",
      "original_text =   A\n",
      "predicted text = A\n",
      "\n",
      "original_text =   form\n",
      "predicted text = fom\n",
      "\n",
      "original_text =   not\n",
      "predicted text = not\n",
      "\n",
      "original_text =   wait\n",
      "predicted text = wait\n",
      "\n",
      "original_text =   will\n",
      "predicted text = will\n",
      "\n",
      "original_text =   Secretary\n",
      "predicted text = Sccretary\n",
      "\n",
      "original_text =   to\n",
      "predicted text = t\n",
      "\n",
      "original_text =   prepared\n",
      "predicted text = propored\n",
      "\n",
      "original_text =   anti-nuclear\n",
      "predicted text = antinmnlend\n",
      "\n",
      "original_text =   action\n",
      "predicted text = action\n",
      "\n",
      "original_text =   of\n",
      "predicted text = of\n",
      "\n",
      "original_text =   peace\n",
      "predicted text = peade\n",
      "\n",
      "original_text =   61-year-old\n",
      "predicted text = ox-yao-old\n",
      "\n",
      "original_text =   The\n",
      "predicted text = The\n",
      "\n",
      "original_text =   -\n",
      "predicted text = -\n",
      "\n",
      "original_text =   being\n",
      "predicted text = kang\n",
      "\n",
      "original_text =   used\n",
      "predicted text = woed\n",
      "\n",
      "original_text =   Pancras\n",
      "predicted text = Panceres\n",
      "\n",
      "original_text =   with\n",
      "predicted text = with\n",
      "\n",
      "original_text =   plans\n",
      "predicted text = pleams\n",
      "\n",
      "original_text =   Measures\n",
      "predicted text = Measwer\n",
      "\n",
      "original_text =   call-out\n",
      "predicted text = call-ont\n",
      "\n",
      "original_text =   750\n",
      "predicted text = Tro\n",
      "\n",
      "original_text =   area\n",
      "predicted text = aren\n",
      "\n",
      "original_text =   .\n",
      "predicted text = .\n",
      "\n",
      "original_text =   Indo-\n",
      "predicted text = Indor-\n",
      "\n",
      "original_text =   Yugoslav\n",
      "predicted text = Yepotav\n",
      "\n",
      "original_text =   been\n",
      "predicted text = been\n",
      "\n",
      "original_text =   and\n",
      "predicted text = and\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   on\n",
      "predicted text = on\n",
      "\n",
      "original_text =   some\n",
      "predicted text = some\n",
      "\n",
      "original_text =   .\n",
      "predicted text = ,\n",
      "\n",
      "original_text =   ,\n",
      "predicted text = ,\n",
      "\n",
      "original_text =   consultative\n",
      "predicted text = conlative\n",
      "\n",
      "original_text =   economic\n",
      "predicted text = coric\n",
      "\n",
      "original_text =   to\n",
      "predicted text = to\n",
      "\n",
      "original_text =   in\n",
      "predicted text = in\n",
      "\n",
      "original_text =   view\n",
      "predicted text = vilew\n",
      "\n",
      "original_text =   of\n",
      "predicted text = of\n",
      "\n",
      "original_text =   more\n",
      "predicted text = more\n",
      "\n",
      "original_text =   work\n",
      "predicted text = vosk\n",
      "\n",
      "original_text =   .\n",
      "predicted text = .\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   on\n",
      "predicted text = an\n",
      "\n",
      "original_text =   emissaries\n",
      "predicted text = amimanes\n",
      "\n",
      "original_text =   arrived\n",
      "predicted text = arived\n",
      "\n",
      "original_text =   Souvanna\n",
      "predicted text = Sousanna\n",
      "\n",
      "original_text =   return\n",
      "predicted text = sehunn\n",
      "\n",
      "original_text =   been\n",
      "predicted text = been\n",
      "\n",
      "original_text =   that\n",
      "predicted text = that\n",
      "\n",
      "original_text =   yet\n",
      "predicted text = not\n",
      "\n",
      "original_text =   by\n",
      "predicted text = by\n",
      "\n",
      "original_text =   himself\n",
      "predicted text = timef\n",
      "\n",
      "original_text =   His\n",
      "predicted text = Sis\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   virtual\n",
      "predicted text = wisteral\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   of\n",
      "predicted text = of\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   of\n",
      "predicted text = of\n",
      "\n",
      "original_text =   Plain\n",
      "predicted text = Mlair\n",
      "\n",
      "original_text =   firmly\n",
      "predicted text = fimky\n",
      "\n",
      "original_text =   recapture\n",
      "predicted text = recapture\n",
      "\n",
      "original_text =   60\n",
      "predicted text = 80\n",
      "\n",
      "original_text =   correspondent\n",
      "predicted text = conepondent\n",
      "\n",
      "original_text =   Vang\n",
      "predicted text = lary\n",
      "\n",
      "original_text =   sunning\n",
      "predicted text = meesnyg\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   10\n",
      "predicted text = 10\n",
      "\n",
      "original_text =   conference\n",
      "predicted text = sanfure\n",
      "\n",
      "original_text =   since\n",
      "predicted text = ta\n",
      "\n",
      "original_text =   leader\n",
      "predicted text = leade\n",
      "\n",
      "original_text =   occasion\n",
      "predicted text = becasion\n",
      "\n",
      "original_text =   simply\n",
      "predicted text = simpl\n",
      "\n",
      "original_text =   supplant\n",
      "predicted text = supplant\n",
      "\n",
      "original_text =   laying\n",
      "predicted text = laying\n",
      "\n",
      "original_text =   .\n",
      "predicted text = .\n",
      "\n",
      "original_text =   of\n",
      "predicted text = of\n",
      "\n",
      "original_text =   is\n",
      "predicted text = is\n",
      "\n",
      "original_text =   Cabinet\n",
      "predicted text = Cabinet\n",
      "\n",
      "original_text =   called\n",
      "predicted text = called\n",
      "\n",
      "original_text =   accomplishments\n",
      "predicted text = ccsnpidrats\n",
      "\n",
      "original_text =   and\n",
      "predicted text = and\n",
      "\n",
      "original_text =   illusion\n",
      "predicted text = illaston\n",
      "\n",
      "original_text =   has\n",
      "predicted text = has\n",
      "\n",
      "original_text =   uncertain\n",
      "predicted text = uncenstain\n",
      "\n",
      "original_text =   Conservative\n",
      "predicted text = Rorservstive\n",
      "\n",
      "original_text =   in\n",
      "predicted text = in\n",
      "\n",
      "original_text =   badly\n",
      "predicted text = badly\n",
      "\n",
      "original_text =   elections\n",
      "predicted text = dections\n",
      "\n",
      "original_text =   one\n",
      "predicted text = oe\n",
      "\n",
      "original_text =   in\n",
      "predicted text = in\n",
      "\n",
      "original_text =   and\n",
      "predicted text = and\n",
      "\n",
      "original_text =   Conservative\n",
      "predicted text = Consernatine\n",
      "\n",
      "original_text =   for\n",
      "predicted text = for\n",
      "\n",
      "original_text =   in\n",
      "predicted text = in\n",
      "\n",
      "original_text =   were\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted text = were\n",
      "\n",
      "original_text =   federal\n",
      "predicted text = federal\n",
      "\n",
      "original_text =   cent\n",
      "predicted text = cent\n",
      "\n",
      "original_text =   C.C.F.\n",
      "predicted text = Cc7.\n",
      "\n",
      "original_text =   Mr.\n",
      "predicted text = Mr.\n",
      "\n",
      "original_text =   an\n",
      "predicted text = al\n",
      "\n",
      "original_text =   ,\n",
      "predicted text = ,\n",
      "\n",
      "original_text =   far\n",
      "predicted text = for\n",
      "\n",
      "original_text =   Canada\n",
      "predicted text = Canado\n",
      "\n",
      "original_text =   national\n",
      "predicted text = notienal\n",
      "\n",
      "original_text =   difficult\n",
      "predicted text = diffieunlt\n",
      "\n",
      "original_text =   of\n",
      "predicted text = of\n",
      "\n",
      "original_text =   that\n",
      "predicted text = that\n",
      "\n",
      "original_text =   .\n",
      "predicted text = .\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   or\n",
      "predicted text = or\n",
      "\n",
      "original_text =   father\n",
      "predicted text = fatker\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   in\n",
      "predicted text = in\n",
      "\n",
      "original_text =   regard\n",
      "predicted text = regarrd\n",
      "\n",
      "original_text =   egghead\n",
      "predicted text = eggshead\n",
      "\n",
      "original_text =   now\n",
      "predicted text = now\n",
      "\n",
      "original_text =   has\n",
      "predicted text = has\n",
      "\n",
      "original_text =   and\n",
      "predicted text = and\n",
      "\n",
      "original_text =   being\n",
      "predicted text = being\n",
      "\n",
      "original_text =   he\n",
      "predicted text = he\n",
      "\n",
      "original_text =   baker\n",
      "predicted text = baler\n",
      "\n",
      "original_text =   has\n",
      "predicted text = hos\n",
      "\n",
      "original_text =   in\n",
      "predicted text = in\n",
      "\n",
      "original_text =   Minister\n",
      "predicted text = Minister\n",
      "\n",
      "original_text =   now\n",
      "predicted text = now\n",
      "\n",
      "original_text =   and\n",
      "predicted text = and\n",
      "\n",
      "original_text =   Mike\n",
      "predicted text = Mike\n",
      "\n",
      "original_text =   ranging\n",
      "predicted text = rarajngs\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   ,\n",
      "predicted text = ,\n",
      "\n",
      "original_text =   tomorrow\n",
      "predicted text = fomaron\n",
      "\n",
      "original_text =   space\n",
      "predicted text = space\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   British\n",
      "predicted text = PriSisn\n",
      "\n",
      "original_text =   they\n",
      "predicted text = thoy\n",
      "\n",
      "original_text =   was\n",
      "predicted text = 2at\n",
      "\n",
      "original_text =   that\n",
      "predicted text = that\n",
      "\n",
      "original_text =   scientific\n",
      "predicted text = sciontific\n",
      "\n",
      "original_text =   to\n",
      "predicted text = to\n",
      "\n",
      "original_text =   Government\n",
      "predicted text = Government\n",
      "\n",
      "original_text =   ,\n",
      "predicted text = ,\n",
      "\n",
      "original_text =   would\n",
      "predicted text = would\n",
      "\n",
      "original_text =   something\n",
      "predicted text = sonetting\n",
      "\n",
      "original_text =   year\n",
      "predicted text = year\n",
      "\n",
      "original_text =   is\n",
      "predicted text = is\n",
      "\n",
      "original_text =   convinced\n",
      "predicted text = convineed\n",
      "\n",
      "original_text =   so\n",
      "predicted text = oo\n",
      "\n",
      "original_text =   constitute\n",
      "predicted text = constitute\n",
      "\n",
      "original_text =   extent\n",
      "predicted text = exlent\n",
      "\n",
      "original_text =   of\n",
      "predicted text = of\n",
      "\n",
      "original_text =   will\n",
      "predicted text = win\n",
      "\n",
      "original_text =   is\n",
      "predicted text = is\n",
      "\n",
      "original_text =   reached\n",
      "predicted text = reached\n",
      "\n",
      "original_text =   first\n",
      "predicted text = fist\n",
      "\n",
      "original_text =   .\n",
      "predicted text = .\n",
      "\n",
      "original_text =   British\n",
      "predicted text = Bwilish\n",
      "\n",
      "original_text =   ,\n",
      "predicted text = ,\n",
      "\n",
      "original_text =   Veronique\n",
      "predicted text = veroniaa\n",
      "\n",
      "original_text =   Foreign\n",
      "predicted text = Foreitn\n",
      "\n",
      "original_text =   Brussels\n",
      "predicted text = Brune's\n",
      "\n",
      "original_text =   rocket\n",
      "predicted text = uocket\n",
      "\n",
      "original_text =   obstacle\n",
      "predicted text = obstacle\n",
      "\n",
      "original_text =   or\n",
      "predicted text = or\n",
      "\n",
      "original_text =   any\n",
      "predicted text = any\n",
      "\n",
      "original_text =   is\n",
      "predicted text = is\n",
      "\n",
      "original_text =   Air\n",
      "predicted text = Sir\n",
      "\n",
      "original_text =   Harold\n",
      "predicted text = Hardd\n",
      "\n",
      "original_text =   at\n",
      "predicted text = ot\n",
      "\n",
      "original_text =   their\n",
      "predicted text = thair\n",
      "\n",
      "original_text =   to\n",
      "predicted text = to\n",
      "\n",
      "original_text =   policy\n",
      "predicted text = policy\n",
      "\n",
      "original_text =   to\n",
      "predicted text = to\n",
      "\n",
      "original_text =   recent\n",
      "predicted text = recent\n",
      "\n",
      "original_text =   made\n",
      "predicted text = mode\n",
      "\n",
      "original_text =   Market\n",
      "predicted text = Market\n",
      "\n",
      "original_text =   meet\n",
      "predicted text = meet\n",
      "\n",
      "original_text =   Mr.\n",
      "predicted text = Mr.\n",
      "\n",
      "original_text =   membership\n",
      "predicted text = membuarhip\n",
      "\n",
      "original_text =   an\n",
      "predicted text = an\n",
      "\n",
      "original_text =   Laos\n",
      "predicted text = lass\n",
      "\n",
      "original_text =   Kennedy\n",
      "predicted text = Penedy\n",
      "\n",
      "original_text =   Home\n",
      "predicted text = Home\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   in\n",
      "predicted text = in\n",
      "\n",
      "original_text =   to\n",
      "predicted text = to\n",
      "\n",
      "original_text =   be\n",
      "predicted text = be\n",
      "\n",
      "original_text =   Labour\n",
      "predicted text = Labour\n",
      "\n",
      "original_text =   asking\n",
      "predicted text = astaing\n",
      "\n",
      "original_text =   Airport\n",
      "predicted text = hispost\n",
      "\n",
      "original_text =   He\n",
      "predicted text = Hhe\n",
      "\n",
      "original_text =   report\n",
      "predicted text = repot\n",
      "\n",
      "original_text =   The\n",
      "predicted text = The\n",
      "\n",
      "original_text =   talks\n",
      "predicted text = taks\n",
      "\n",
      "original_text =   ,\n",
      "predicted text = ,\n",
      "\n",
      "original_text =   discussions\n",
      "predicted text = diaussions\n",
      "\n",
      "original_text =   ,\n",
      "predicted text = ,\n",
      "\n",
      "original_text =   two\n",
      "predicted text = two\n",
      "\n",
      "original_text =   was\n",
      "predicted text = was\n",
      "\n",
      "original_text =   expansion\n",
      "predicted text = agression\n",
      "\n",
      "original_text =   alike\n",
      "predicted text = alite\n",
      "\n",
      "original_text =   noted\n",
      "predicted text = nated\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   lead\n",
      "predicted text = lend\n",
      "\n",
      "original_text =   conference\n",
      "predicted text = conferene\n",
      "\n",
      "original_text =   nuclear\n",
      "predicted text = nudear\n",
      "\n",
      "original_text =   \"\n",
      "predicted text = \"\n",
      "\n",
      "original_text =   and\n",
      "predicted text = and\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   .\n",
      "predicted text = .\n",
      "\n",
      "original_text =   followed\n",
      "predicted text = folened\n",
      "\n",
      "original_text =   Kennedy\n",
      "predicted text = Kennedy\n",
      "\n",
      "original_text =   Macmillan\n",
      "predicted text = Macillan\n",
      "\n",
      "original_text =   Mr.\n",
      "predicted text = Mr.\n",
      "\n",
      "original_text =   a\n",
      "predicted text = a\n",
      "\n",
      "original_text =   to\n",
      "predicted text = to\n",
      "\n",
      "original_text =   Vienna\n",
      "predicted text = Vienna\n",
      "\n",
      "original_text =   in\n",
      "predicted text = in\n",
      "\n",
      "original_text =   fly\n",
      "predicted text = tio\n",
      "\n",
      "original_text =   Adenauer\n",
      "predicted text = Aenaner\n",
      "\n",
      "original_text =   of\n",
      "predicted text = of\n",
      "\n",
      "original_text =   should\n",
      "predicted text = should\n",
      "\n",
      "original_text =   smiling\n",
      "predicted text = suuiling\n",
      "\n",
      "original_text =   Krushchov\n",
      "predicted text = Lustchov\n",
      "\n",
      "original_text =   talks\n",
      "predicted text = talks\n",
      "\n",
      "original_text =   .\n",
      "predicted text = .\n",
      "\n",
      "original_text =   up\n",
      "predicted text = up\n",
      "\n",
      "original_text =   Party\n",
      "predicted text = Party\n",
      "\n",
      "original_text =   gave\n",
      "predicted text = gave\n",
      "\n",
      "original_text =   war\n",
      "predicted text = war\n",
      "\n",
      "original_text =   stable\n",
      "predicted text = stoble\n",
      "\n",
      "original_text =   good\n",
      "predicted text = good\n",
      "\n",
      "original_text =   airport\n",
      "predicted text = airpot\n",
      "\n",
      "original_text =   President\n",
      "predicted text = President\n",
      "\n",
      "original_text =   Boeing\n",
      "predicted text = Boeing\n",
      "\n",
      "original_text =   Mr.\n",
      "predicted text = Mr.\n",
      "\n",
      "original_text =   delighted\n",
      "predicted text = aslighhed\n",
      "\n",
      "original_text =   surround\n",
      "predicted text = Purround\n",
      "\n",
      "original_text =   before\n",
      "predicted text = befoe\n",
      "\n",
      "original_text =   hoped\n",
      "predicted text = hoped\n",
      "\n",
      "original_text =   \"\n",
      "predicted text = \"\n",
      "\n",
      "original_text =   Replying\n",
      "predicted text = Replyig\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   he\n",
      "predicted text = he\n",
      "\n",
      "original_text =   striven\n",
      "predicted text = striwven\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   MR.\n",
      "predicted text = Mr.\n",
      "\n",
      "original_text =   missile\n",
      "predicted text = missile\n",
      "\n",
      "original_text =   of\n",
      "predicted text = of\n",
      "\n",
      "original_text =   .\n",
      "predicted text = .\n",
      "\n",
      "original_text =   to\n",
      "predicted text = to\n",
      "\n",
      "original_text =   and\n",
      "predicted text = and\n",
      "\n",
      "original_text =   long\n",
      "predicted text = lony\n",
      "\n",
      "original_text =   annual\n",
      "predicted text = anmal\n",
      "\n",
      "original_text =   Workers\n",
      "predicted text = Nakas\n",
      "\n",
      "original_text =   was\n",
      "predicted text = was\n",
      "\n",
      "original_text =   conscious\n",
      "predicted text = coions\n",
      "\n",
      "original_text =   Polaris\n",
      "predicted text = Pelars\n",
      "\n",
      "original_text =   even\n",
      "predicted text = even\n",
      "\n",
      "original_text =   their\n",
      "predicted text = their\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   an\n",
      "predicted text = an\n",
      "\n",
      "original_text =   it\n",
      "predicted text = it\n",
      "\n",
      "original_text =   negotiate\n",
      "predicted text = mgotate\n",
      "\n",
      "original_text =   .\n",
      "predicted text = .\n",
      "\n",
      "original_text =   he\n",
      "predicted text = the\n",
      "\n",
      "original_text =   and\n",
      "predicted text = and\n",
      "\n",
      "original_text =   apply\n",
      "predicted text = afly\n",
      "\n",
      "original_text =   Soviet\n",
      "predicted text = Sviet\n",
      "\n",
      "original_text =   ,\n",
      "predicted text = ,\n",
      "\n",
      "original_text =   for\n",
      "predicted text = fe\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   was\n",
      "predicted text = was\n",
      "\n",
      "original_text =   ,\n",
      "predicted text = ,\n",
      "\n",
      "original_text =   two\n",
      "predicted text = tao\n",
      "\n",
      "original_text =   more\n",
      "predicted text = moare\n",
      "\n",
      "original_text =   conference\n",
      "predicted text = conferme\n",
      "\n",
      "original_text =   reflect\n",
      "predicted text = iefet\n",
      "\n",
      "original_text =   Gaitskell\n",
      "predicted text = Gaitstell\n",
      "\n",
      "original_text =   ,\n",
      "predicted text = ,\n",
      "\n",
      "original_text =   reports\n",
      "predicted text = neporats\n",
      "\n",
      "original_text =   recommending\n",
      "predicted text = necommandeing\n",
      "\n",
      "original_text =   emphasis\n",
      "predicted text = cnphois\n",
      "\n",
      "original_text =   To\n",
      "predicted text = is\n",
      "\n",
      "original_text =   should\n",
      "predicted text = should\n",
      "\n",
      "original_text =   of\n",
      "predicted text = o\n",
      "\n",
      "original_text =   hope\n",
      "predicted text = hogpr\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   of\n",
      "predicted text = of\n",
      "\n",
      "original_text =   prepared\n",
      "predicted text = prepond\n",
      "\n",
      "original_text =   disarmament\n",
      "predicted text = Kfemmament\n",
      "\n",
      "original_text =   the\n",
      "predicted text = the\n",
      "\n",
      "original_text =   opportunity\n",
      "predicted text = sposntimunty\n",
      "\n",
      "original_text =   that\n",
      "predicted text = that\n",
      "\n"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "for x in out:\n",
    "    print(\"original_text =  \", valid_original_text[i])\n",
    "    print(\"predicted text = \", end = '')\n",
    "    for p in x:  \n",
    "        if int(p) != -1:\n",
    "            print(char_list[int(p)], end = '')       \n",
    "    print('\\n')\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9d25f19d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "jaro : 0.907258677606851\n",
      "ratio: 0.8745419703261236\n"
     ]
    }
   ],
   "source": [
    "import Levenshtein as lv\n",
    "\n",
    "total_jaro = 0\n",
    "total_rati = 0\n",
    "# see the results\n",
    "for i, x in enumerate(out):\n",
    "    letters=''\n",
    "    for p in x:\n",
    "        if int(p) != -1:\n",
    "            letters+=char_list[int(p)]\n",
    "    total_jaro+=lv.jaro(letters, valid_original_text[i])\n",
    "    total_rati+=lv.ratio(letters, valid_original_text[i])\n",
    "\n",
    "print('jaro :', total_jaro/len(out))\n",
    "print('ratio:', total_rati/len(out))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6cb55c4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(876, 32, 128, 1)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b5434fc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b7ab6bb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "6ccd7de1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x19f99938fd0>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = cv2.imread('C:/Users/nguye/OneDrive/Code/handwritting_text/dataset/a04/a04-000/a04-000-05-00.png',cv2.IMREAD_GRAYSCALE)\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b9e18a80",
   "metadata": {},
   "outputs": [],
   "source": [
    "img = process_image(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "074b157b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 128, 1)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "c393b95d",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = model.predict(img.reshape(1,32,128,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "50403799",
   "metadata": {},
   "outputs": [],
   "source": [
    "decoded = K.ctc_decode(prediction, \n",
    "                       input_length=np.ones(prediction.shape[0]) * prediction.shape[1],\n",
    "                       greedy=True)[0][0]\n",
    "out = K.get_value(decoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "951640f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted text = to\n",
      "\n"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "for x in out:\n",
    "    print(\"predicted text = \", end = '')\n",
    "    for p in x:  \n",
    "        if int(p) != -1:\n",
    "            print(char_list[int(p)], end = '')       \n",
    "    print('\\n')\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdde4d61",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}