| paper | download training and evaluation data | download failure cases and cleansed label |
PyTorch implementation of our four-stage STR framework, that most existing STR models fit into.
Using this framework allows for the module-wise contributions to performance in terms of accuracy, speed, and memory demand, under one consistent set of training and evaluation datasets.
Such analyses clean up the hindrance on the current comparisons to understand the performance gain of the existing modules.
- This work was tested with PyTorch 1.0.1, CUDA 9.0, python 3.6 and Ubuntu 16.04.
You may needpip3 install torch==1.0.1 - requirements : lmdb, pillow, torchvision, nltk, torch-baidu-ctc
pip3 install lmdb pillow torchvision nltk torch-baidu-ctc
Download lmdb dataset for traininig and evaluation from here
data_lmdb_release.zip contains below.
training datasets : MJSynth (MJ)[1] and SynthText (ST)[2]
validation datasets : the union of the training sets IC13[3], IC15[4], IIIT[5], and SVT[6].
evaluation datasets : benchmark evaluation datasets, consist of IIIT[5], SVT[6], IC03[7], IC13[3], IC15[4], SVTP[8], and CUTE[9].
- Train CRNN[10] model
CUDA_VISIBLE_DEVICES=0 python3 train.py \
--train_data data_lmdb_release/training --valid_data data_lmdb_release/validation \
--select_data MJ-ST --batch_ratio 0.5-0.5 \
--Transformation None --FeatureExtraction VGG --SequenceModeling BiLSTM --Prediction CTC
- Test CRNN[10] model
CUDA_VISIBLE_DEVICES=0 python3 test.py \
--eval_data data_lmdb_release/evaluation --benchmark_all_eval \
--Transformation None --FeatureExtraction VGG --SequenceModeling BiLSTM --Prediction CTC \
--saved_model saved_models/None-VGG-BiLSTM-CTC-Seed1111/best_accuracy.pth
- Try to train and test our best accuracy combination (TPS-ResNet-BiLSTM-Attn) also.
CUDA_VISIBLE_DEVICES=0 python3 train.py \
--train_data data_lmdb_release/training --valid_data data_lmdb_release/validation \
--select_data MJ-ST --batch_ratio 0.5-0.5 \
--Transformation TPS --FeatureExtraction ResNet --SequenceModeling BiLSTM --Prediction Attn
CUDA_VISIBLE_DEVICES=0 python3 test.py \
--eval_data data_lmdb_release/evaluation --benchmark_all_eval \
--Transformation TPS --FeatureExtraction ResNet --SequenceModeling BiLSTM --Prediction Attn \
--saved_model saved_models/TPS-ResNet-BiLSTM-Attn-Seed1111/best_accuracy.pth
--train_data: forder path to training lmdb dataset.--valid_data: forder path to validation lmdb dataset.--eval_data: forder path to evaluation (with test.py) lmdb dataset.--select_data: select training data. default is MJ-ST, which means MJ and ST used as training data.--batch_ratio: assign ratio for each selected data in the batch. default is 0.5-0.5, which means 50% of the batch is filled with MJ and the other 50% of the batch is filled ST.--Transformation: select Transformation module [None | TPS].--FeatureExtraction: select FeatureExtraction module [VGG | RCNN | ResNet].--SequenceModeling: select SequenceModeling module [None | BiLSTM].--Prediction: select Prediction module [CTC | Attn].--saved_model: assign saved model to evaluation.--benchmark_all_eval: evaluate with 10 evaluation dataset versions, same with Table 1 in our paper.
Download failure cases and cleansed label from here
image_release.zip contains failure case images and benchmark evaluation images with cleansed label.
pip3 install fire
python3 create_lmdb_dataset.py --inputPath data/ --gtFile data/gt.txt --outputPath result/
At this time, gt.txt should be {imagepath}\t{label}\n
For example
test/word_1.png Tiredness
test/word_2.png kills
test/word_3.png A
...
This implementation has been based on these repository crnn.pytorch, ocr_attention.
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[2] A. Gupta, A. Vedaldi, and A. Zisserman. Synthetic data fortext localisation in natural images. In CVPR, 2016.
[3] D. Karatzas, F. Shafait, S. Uchida, M. Iwamura, L. G. i Big-orda, S. R. Mestre, J. Mas, D. F. Mota, J. A. Almazan, andL. P. De Las Heras. ICDAR 2013 robust reading competition. In ICDAR, pages 1484–1493, 2013.
[4] D. Karatzas, L. Gomez-Bigorda, A. Nicolaou, S. Ghosh, A. Bagdanov, M. Iwamura, J. Matas, L. Neumann, V. R.Chandrasekhar, S. Lu, et al. ICDAR 2015 competition on ro-bust reading. In ICDAR, pages 1156–1160, 2015.
[5] A. Mishra, K. Alahari, and C. Jawahar. Scene text recognition using higher order language priors. In BMVC, 2012.
[6] K. Wang, B. Babenko, and S. Belongie. End-to-end scenetext recognition. In ICCV, pages 1457–1464, 2011.
[7] S. M. Lucas, A. Panaretos, L. Sosa, A. Tang, S. Wong, andR. Young. ICDAR 2003 robust reading competitions. In ICDAR, pages 682–687, 2003.
[8] T. Q. Phan, P. Shivakumara, S. Tian, and C. L. Tan. Recognizing text with perspective distortion in natural scenes. In ICCV, pages 569–576, 2013.
[9] A. Risnumawan, P. Shivakumara, C. S. Chan, and C. L. Tan. A robust arbitrary text detection system for natural scene images. In ESWA, volume 41, pages 8027–8048, 2014.
[10] B. Shi, X. Bai, and C. Yao. An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. In TPAMI, volume 39, pages2298–2304. 2017.
If you find this work useful for your research, please cite:
@article{baek2019STRcomparisons,
title={What is wrong with scene text recognition model comparisons? dataset and model analysis},
author={Baek, Jeonghun and Kim, Geewook and Lee, Junyeop and Park, Sungrae and Han, Dongyoon and Yun, Sangdoo and Oh, Seong Joon and Lee, Hwalsuk},
journal={arXiv preprint arXiv:1904.01906},
year={2019}
}
Feel free to contact me if there is any question (Jeonghun Baek [email protected]).
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