Skip to content

This project implement basic OCR for Vietnamese from scratch with Pytorch, using CNN and BidirectionalLSTM

License

Notifications You must be signed in to change notification settings

sonhm3029/Vietnamese-OCR-from-scratch-pytorch

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

11 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Adapting-OCR

Pytorch implementation of our paper Adapting OCR with limited labels

Qualitative Result of our Base, self-trained and hybrid model for English (left) and Hindi (right) datasets. Here ST+FT refers to the model trained using the proposed hybrid approach.

Dependency

  • This work was tested with PyTorch 1.2.0, CUDA 9.0, python 3.6 and Ubuntu 16.04.
  • requirements can be found in the file.
  • Also, please do a pip install pytorch-pretrained-bert as one of our kind contributors pointed out :)
  • command to create environment from the file is conda create -n pytorch1.4 --file env.txt
  • To activate the environment use: source activate pytorch1.4

Training

  • Supervised training

python -m train --name exp1 --path path/to/data

  • Main arguments

    • --name: creates a directory where checkpoints will be stored
    • --path: path to dataset.
    • --imgdir: dir name of dataset
  • Semi-supervised training

python -m train_semi_supervised --name exp1 --path path --source_dir src_dirname --target_dir tgt_dirname --schedule --noise --alpha=1

  • Main arguments
    • --name: creates a directory where checkpoints will be stored
    • --path: path to datasets
    • --source_dir: labelled data directory on which ocr was trained
    • --target_dir: unlabeled data directory on which we want to adapt ocr
    • --percent: percentage of unlabeled data to include in self-training
    • --schedule: will include STLR scheduler while training
    • --train_on_pred: will treat top-predictions as targets
    • --noise: will add gaussian noise to images while training
    • --alpha: set to 1 to include the mixup criterion
    • --combine_scoring: will also take into account the scores outputted by a language model

Note: --combine_scoring works only with line images not word images

  • Data
    • Use trdg to generate synthetic data. The script for data generation is included scrips/generate_data.sh.
    • Download two different fonts and keep the data pertaining to each font in source and target dirs.
    • Use one of the fonts to train data from scratch in a supervised manner.
    • Then finetune the trained model on target data using semi-supervised learning
    • A sample lexicon is provided in words.txt. Download different lexicon as per need.

References

  • The OCR architecture is a CNN-LSTM model borrowed from here
  • The mixup criterion code is borrowed from here
  • STLR is borrowed from this paper

About

This project implement basic OCR for Vietnamese from scratch with Pytorch, using CNN and BidirectionalLSTM

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published