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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.

framework

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):
    2. SynthText (ST):
    3. WikiText103, which is only used for pre-trainig language models:

  • 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:

Model IC13 SVT IIIT IC15 SVTP CUTE AVG
ABINet-SV 97.1 92.7 95.2 84.0 86.7 88.5 91.4
ABINet-LV 97.0 93.4 96.4 85.9 89.5 89.2 92.7

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

Run Demo

python demo.py --config=configs/train_abinet.yaml --input=figs/test

Additional flags:

  • --config /path/to/config set the path of configuration file
  • --input /path/to/image-directory set the path of image directory or wildcard path, e.g, --input='figs/test/*.png'
  • --checkpoint /path/to/checkpoint set the path of trained model
  • --cuda [-1|0|1|2|3...] set the cuda id, by default -1 is set and stands for cpu
  • --model_eval [alignment|vision] which sub-model to use
  • --image_only disable dumping visualization of attention masks

Visualization

Successful and failure cases on low-quality images:

cases

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.