| Original Code | paper | training and evaluation data | pretrained model | Baidu ver(passwd:rryk) |

Official 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 1.3.1, CUDA 10.1, python 3.6 and Ubuntu 16.04.
  You may need pip3 install torch==1.3.1.
  In the paper, expriments were performed with PyTorch 0.4.1, CUDA 9.0.
• requirements : lmdb, pillow, torchvision, nltk, natsort

pip3 install lmdb pillow torchvision nltk natsort

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].

1. Download pretrained model from here
2. Add image files to test into demo_image/
3. Run demo.py (add --sensitive option if you use case-sensitive model)

CUDA_VISIBLE_DEVICES=0 python3 demo.py \
--Transformation TPS --FeatureExtraction ResNet --SequenceModeling BiLSTM --Prediction Attn \
--image_folder demo_image/ \
--saved_model TPS-ResNet-BiLSTM-Attn.pth

demo images	TRBA (TPS-ResNet-BiLSTM-Attn)	TRBA (case-sensitive version)
	available	Available
	shakeshack	SHARESHACK
	london	Londen
	greenstead	Greenstead
	toast	TOAST
	merry	MERRY
	underground	underground
	ronaldo	RONALDO
	bally	BALLY
	university	UNIVERSITY

1. 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

2. Test CRNN[10] model. If you want to evaluate IC15-2077, check data filtering part.

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

3. Try to train and test our best accuracy model TRBA (TPS-ResNet-BiLSTM-Attn) also. (download pretrained 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 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: folder path to training lmdb dataset.
• --valid_data: folder path to validation lmdb dataset.
• --eval_data: folder 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.
• --data_filtering_off: skip data filtering when creating LmdbDataset.
• --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.

1. Create your own lmdb dataset.

pip3 install fire
python3 create_lmdb_dataset.py --inputPath data/ --gtFile data/gt.txt --outputPath result/

The structure of data folder as below.

data
├── gt.txt
└── test
    ├── word_1.png
    ├── word_2.png
    ├── word_3.png
    └── ...

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
...

2. Modify --select_data, --batch_ratio, and opt.character, see this issue.

This implementation has been based on these repository crnn.pytorch, ocr_attention. Original Code

[1] M. Jaderberg, K. Simonyan, A. Vedaldi, and A. Zisserman. Synthetic data and artificial neural networks for natural scenetext recognition. In Workshop on Deep Learning, NIPS, 2014.
[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.

• WebDemo : https://demo.ocr.clova.ai/
  Combination of Clova AI detection and recognition, additional/advanced features used for KOR/JPN.
• Repo of detection : https://github.com/clovaai/CRAFT-pytorch

Please consider citing this work in your publications if it helps your research.

@inproceedings{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},
  booktitle = {International Conference on Computer Vision (ICCV)},
  year={2019},
  pubstate={published},
  tppubtype={inproceedings}
}

Feel free to contact us if there is any question:
for code/paper Jeonghun Baek [email protected]; for collaboration [email protected] (our team leader).

Copyright (c) 2019-present NAVER Corp.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.