Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition

The official code of ABINet (CVPR 2021, Oral).

ABINet uses a vision model and an explicit language model to recognize text in the wild, which are trained in end-to-end way. The language model (BCN) achieves bidirectional language representation in simulating cloze test, additionally utilizing iterative correction strategy.

Runtime Environment

• We provide a pre-built docker image using the Dockerfile from docker/Dockerfile

• Running in Docker

  $ [email protected]:FangShancheng/ABINet.git
  $ docker run --gpus all --rm -ti --ipc=host -v $(pwd)/ABINet:/app fangshancheng/fastai:torch1.1 /bin/bash
  

• (Untested) Or using the dependencies

  pip install -r requirements.txt
  

Datasets

• Training datasets

  1. MJSynth (MJ):
     • Use tools/create_lmdb_dataset.py to convert images into LMDB dataset
     • LMDB dataset BaiduNetdisk(passwd:n23k)
  2. SynthText (ST):
     • Use tools/crop_by_word_bb.py to crop images from original SynthText dataset, and convert images into LMDB dataset by tools/create_lmdb_dataset.py
     • LMDB dataset BaiduNetdisk(passwd:n23k)
  3. WikiText103, which is only used for pre-trainig language models:
     • Use notebooks/prepare_wikitext103.ipynb to convert text into CSV format.
     • CSV dataset BaiduNetdisk(passwd:dk01)

• Evaluation datasets, LMDB datasets can be downloaded from BaiduNetdisk(passwd:1dbv), GoogleDrive.

  1. ICDAR 2013 (IC13)
  2. ICDAR 2015 (IC15)
  3. IIIT5K Words (IIIT)
  4. Street View Text (SVT)
  5. Street View Text-Perspective (SVTP)
  6. CUTE80 (CUTE)

• The structure of data directory is

  data
  ├── charset_36.txt
  ├── evaluation
  │   ├── CUTE80
  │   ├── IC13_857
  │   ├── IC15_1811
  │   ├── IIIT5k_3000
  │   ├── SVT
  │   └── SVTP
  ├── training
  │   ├── MJ
  │   │   ├── MJ_test
  │   │   ├── MJ_train
  │   │   └── MJ_valid
  │   └── ST
  ├── WikiText-103.csv
  └── WikiText-103_eval_d1.csv
  

Pretrained Models

Get the pretrained models from BaiduNetdisk(passwd:kwck), GoogleDrive. Performances of the pretrained models are summaried as follows:

Training

1. Pre-train vision model

   CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --config=configs/pretrain_vision_model.yaml
   

2. Pre-train language model

   CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --config=configs/pretrain_language_model.yaml
   

3. Train ABINet

   CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --config=configs/train_abinet.yaml
   

Note:

• You can set the checkpoint path for vision and language models separately for specific pretrained model, or set to None to train from scratch

Evaluation

CUDA_VISIBLE_DEVICES=0 python main.py --config=configs/train_abinet.yaml --phase test --image_only

Additional flags:

• --checkpoint /path/to/checkpoint set the path of evaluation model
• --test_root /path/to/dataset set the path of evaluation dataset
• --model_eval [alignment|vision] which sub-model to evaluate
• --image_only disable dumping visualization of attention masks

Visualization

Successful and failure cases on low-quality images:

Citation

If you find our method useful for your reserach, please cite

@article{fang2021read,
  title={Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition},
  author={Fang, Shancheng and Xie, Hongtao and Wang, Yuxin and Mao, Zhendong and Zhang, Yongdong},
    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2021}
}

License

This project is only free for academic research purposes, licensed under the 2-clause BSD License - see the LICENSE file for details.

Feel free to contact [email protected] if you have any questions.