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An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition

Introduction

[ALGORITHM]

@article{shi2016end,
  title={An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition},
  author={Shi, Baoguang and Bai, Xiang and Yao, Cong},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  year={2016}
}

Results and Models

Train Dataset

trainset instance_num repeat_num note
Syn90k 8919273 1 synth

Test Dataset

testset instance_num note
IIIT5K 3000 regular
SVT 647 regular
IC13 1015 regular
IC15 2077 irregular
SVTP 645 irregular
CT80 288 irregular

Results and models

methods Regular Text Irregular Text download
methods IIIT5K SVT IC13 IC15 SVTP CT80
CRNN 80.5 81.5 86.5 54.1 59.1 55.6 model | log

NRTR

Introduction

[ALGORITHM]

@inproceedings{sheng2019nrtr,
  title={NRTR: A no-recurrence sequence-to-sequence model for scene text recognition},
  author={Sheng, Fenfen and Chen, Zhineng and Xu, Bo},
  booktitle={2019 International Conference on Document Analysis and Recognition (ICDAR)},
  pages={781--786},
  year={2019},
  organization={IEEE}
}

[BACKBONE]

@inproceedings{li2019show,
  title={Show, attend and read: A simple and strong baseline for irregular text recognition},
  author={Li, Hui and Wang, Peng and Shen, Chunhua and Zhang, Guyu},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={33},
  number={01},
  pages={8610--8617},
  year={2019}
}

Dataset

Train Dataset

trainset instance_num repeat_num source
SynthText 7266686 1 synth
Syn90k 8919273 1 synth

Test Dataset

testset instance_num type
IIIT5K 3000 regular
SVT 647 regular
IC13 1015 regular
IC15 2077 irregular
SVTP 645 irregular
CT80 288 irregular

Results and Models

Methods Backbone Regular Text Irregular Text download
IIIT5K SVT IC13 IC15 SVTP CT80
NRTR R31-1/16-1/8 93.9 90.0 93.5 74.5 78.5 86.5 model | log
NRTR R31-1/8-1/4 94.7 87.5 93.3 75.1 78.9 87.9 model | log

Notes:

  • R31-1/16-1/8 means the height of feature from backbone is 1/16 of input image, where 1/8 for width.

  • R31-1/8-1/4 means the height of feature from backbone is 1/8 of input image, where 1/4 for width.

RobustScanner: Dynamically Enhancing Positional Clues for Robust Text Recognition

Introduction

[ALGORITHM]

@inproceedings{yue2020robustscanner,
  title={RobustScanner: Dynamically Enhancing Positional Clues for Robust Text Recognition},
  author={Yue, Xiaoyu and Kuang, Zhanghui and Lin, Chenhao and Sun, Hongbin and Zhang, Wayne},
  booktitle={European Conference on Computer Vision},
  year={2020}
}

Dataset

Train Dataset

trainset instance_num repeat_num source
icdar_2011 3567 20 real
icdar_2013 848 20 real
icdar2015 4468 20 real
coco_text 42142 20 real
IIIT5K 2000 20 real
SynthText 2400000 1 synth
SynthAdd 1216889 1 synth, 1.6m in [1]
Syn90k 2400000 1 synth

Test Dataset

testset instance_num type
IIIT5K 3000 regular
SVT 647 regular
IC13 1015 regular
IC15 2077 irregular
SVTP 645 irregular, 639 in [1]
CT80 288 irregular

Results and Models

Methods GPUs Regular Text Irregular Text download
IIIT5K SVT IC13 IC15 SVTP CT80
RobustScanner 16 95.1 89.2 93.1 77.8 80.3 90.3 model | log

References

[1] Li, Hui and Wang, Peng and Shen, Chunhua and Zhang, Guyu. Show, attend and read: A simple and strong baseline for irregular text recognition. In AAAI 2019.

Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition

Introduction

[ALGORITHM]

@inproceedings{li2019show,
  title={Show, attend and read: A simple and strong baseline for irregular text recognition},
  author={Li, Hui and Wang, Peng and Shen, Chunhua and Zhang, Guyu},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={33},
  number={01},
  pages={8610--8617},
  year={2019}
}

Dataset

Train Dataset

trainset instance_num repeat_num source
icdar_2011 3567 20 real
icdar_2013 848 20 real
icdar2015 4468 20 real
coco_text 42142 20 real
IIIT5K 2000 20 real
SynthText 2400000 1 synth
SynthAdd 1216889 1 synth, 1.6m in [1]
Syn90k 2400000 1 synth

Test Dataset

testset instance_num type
IIIT5K 3000 regular
SVT 647 regular
IC13 1015 regular
IC15 2077 irregular
SVTP 645 irregular, 639 in [1]
CT80 288 irregular

Results and Models

Methods Backbone Decoder Regular Text Irregular Text download
IIIT5K SVT IC13 IC15 SVTP CT80
SAR R31-1/8-1/4 ParallelSARDecoder 95.0 89.6 93.7 79.0 82.2 88.9 model | log
SAR R31-1/8-1/4 SequentialSARDecoder 95.2 88.7 92.4 78.2 81.9 89.6 model | log

Chinese Dataset

Results and Models

Methods Backbone Decoder download
SAR R31-1/8-1/4 ParallelSARDecoder model | log | dict

Notes:

  • R31-1/8-1/4 means the height of feature from backbone is 1/8 of input image, where 1/4 for width.

  • We did not use beam search during decoding.

  • We implemented two kinds of decoder. Namely, ParallelSARDecoder and SequentialSARDecoder.

    • ParallelSARDecoder: Parallel decoding during training with LSTM layer. It would be faster.

    • SequentialSARDecoder: Sequential Decoding during training with LSTMCell. It would be easier to understand.

  • For train dataset.

    • We did not construct distinct data groups (20 groups in [1]) to train the model group-by-group since it would render model training too complicated.

    • Instead, we randomly selected 2.4m patches from Syn90k, 2.4m from SynthText and 1.2m from SynthAdd, and grouped all data together. See config for details.

  • We used 48 GPUs with total_batch_size = 64 * 48 in the experiment above to speedup training, while keeping the initial lr = 1e-3 unchanged.

References

[1] Li, Hui and Wang, Peng and Shen, Chunhua and Zhang, Guyu. Show, attend and read: A simple and strong baseline for irregular text recognition. In AAAI 2019.

SATRN

Introduction

[ALGORITHM]

@article{junyeop2019recognizing,
  title={On Recognizing Texts of Arbitrary Shapes with 2D Self-Attention},
  author={Junyeop Lee, Sungrae Park, Jeonghun Baek, Seong Joon Oh, Seonghyeon Kim, Hwalsuk Lee},
  year={2019}
}

Dataset

Train Dataset

trainset instance_num repeat_num source
SynthText 7266686 1 synth
Syn90k 8919273 1 synth

Test Dataset

testset instance_num type
IIIT5K 3000 regular
SVT 647 regular
IC13 1015 regular
IC15 2077 irregular
SVTP 645 irregular
CT80 288 irregular

Results and Models

Methods Regular Text Irregular Text download
IIIT5K SVT IC13 IC15 SVTP CT80
Satrn 96.1 93.5 95.7 84.1 88.5 90.3 model | log
Satrn_small 94.7 91.3 95.4 81.9 85.9 86.5 model | log

SegOCR Simple Baseline.

Introduction

[ALGORITHM]

@unpublished{key,
  title={SegOCR Simple Baseline.},
  author={},
  note={Unpublished Manuscript},
  year={2021}
}

Dataset

Train Dataset

trainset instance_num repeat_num source
SynthText 7266686 1 synth

Test Dataset

testset instance_num type
IIIT5K 3000 regular
SVT 647 regular
IC13 1015 regular
CT80 288 irregular

Results and Models

Backbone Neck Head Regular Text Irregular Text download
IIIT5K SVT IC13 CT80
R31-1/16 FPNOCR 1x 90.9 81.8 90.7 80.9 model | log

Notes:

  • R31-1/16 means the size (both height and width ) of feature from backbone is 1/16 of input image.

  • 1x means the size (both height and width) of feature from head is the same with input image.

CRNN with TPS based STN

Introduction

[ALGORITHM]

@article{shi2016end,
  title={An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition},
  author={Shi, Baoguang and Bai, Xiang and Yao, Cong},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  year={2016}
}

[PREPROCESSOR]

@article{shi2016robust,
  title={Robust Scene Text Recognition with Automatic Rectification},
  author={Shi, Baoguang and Wang, Xinggang and Lyu, Pengyuan and Yao,
  Cong and Bai, Xiang},
  year={2016}
}

Results and Models

Train Dataset

trainset instance_num repeat_num note
Syn90k 8919273 1 synth

Test Dataset

testset instance_num note
IIIT5K 3000 regular
SVT 647 regular
IC13 1015 regular
IC15 2077 irregular
SVTP 645 irregular
CT80 288 irregular

Results and models

methods Regular Text Irregular Text download
IIIT5K SVT IC13 IC15 SVTP CT80
CRNN-STN 80.8 81.3 85.0 59.6 68.1 53.8 model | log
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