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Official Pytorch implementations of CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition(IJCV)

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CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition

The official code of CDistNet.

Paper Link : Arxiv Link

What's News

  • [2023-08]🌟 Our paper is accepted by IJCV
  • [2022-01]🌟 Our code is released in github
  • [2021-11]🌟 The paper can be read in Arixv: http://arxiv.org/abs/2111.11011

pipline

To Do List

  • HA-IC13 & CA-IC13
  • Pre-train model
  • Cleaned Code
  • Document
  • Distributed Training

Two New Datasets

we test other sota method in HA-IC13 and CA-IC13 datasets.

HA_CA CDistNet has a performance advantage over other SOTA methods as the character distance increases (1-6)

HA-IC13

Method 1 2 3 4 5 6 Code & Pretrain model
VisionLAN (ICCV 2021) 93.58 92.88 89.97 82.26 72.23 61.03 Offical Code
ABINet (CVPR 2021 ) 95.92 95.22 91.95 85.76 73.75 64.99 Offical Code
RobustScanner* (ECCV 2020) 96.15 95.33 93.23 88.91 81.10 71.53 --
Transformer-baseline* 96.27 95.45 92.42 86.46 79.35 72.46 --
CDistNet 96.62 96.15 94.28 89.96 83.43 77.71 --

CA-IC13

Method 1 2 3 4 5 6 Code & Pretrain model
VisionLAN (ICCV 2021) 94.87 92.77 84.01 75.03 64.29 52.74 Offical Code
ABINet (CVPR 2021 ) 96.62 95.92 87.86 76.31 65.46 54.49 Offical Code
RobustScanner* (ECCV 2020) 95.22 94.87 85.30 76.55 68.38 60.79 --
Transformer-baseline* 95.68 94.40 85.88 75.85 65.93 58.58 --
CDistNet 96.27 95.57 88.45 79.58 70.36 63.13 --

Datasets

The datasets are same as ABINet

Environment

package you can find in env_cdistnet.yaml.

#Installed
conda create -n CDistNet python=3.7
conda install pytorch==1.5.1 torchvision==0.6.1 cudatoolkit=9.2 -c pytorch
pip install opencv-python mmcv notebook numpy einops tensorboardX Pillow thop timm tornado tqdm matplotlib lmdb

Pretrained Models

Get the pretrained models from BaiduNetdisk(passwd:d6jd), GoogleDrive. (We both offer training log and result.csv in same file.) The pretrained model should set in models/reconstruct_CDistNetv3_3_10

Performances of the pretrained models are summaried as follows:

Train

CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --config=configs/CDistNet_config.py

Eval

CUDA_VISIBLE_DEVICES=0 python eval.py --config=configs/CDistNet_config.py

Citation

@article{Zheng2021CDistNetPM,
  title={CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition},
  author={Tianlun Zheng and Zhineng Chen and Shancheng Fang and Hongtao Xie and Yu-Gang Jiang},
  journal={ArXiv},
  year={2021},
  volume={abs/2111.11011}
}

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Official Pytorch implementations of CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition(IJCV)

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