Text recognition (optical character recognition) with deep learning methods.

Overview

What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis

| paper | training and evaluation data | failure cases and cleansed label | 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.

Honors

Based on this framework, we recorded the 1st place of ICDAR2013 focused scene text, ICDAR2019 ArT and 3rd place of ICDAR2017 COCO-Text, ICDAR2019 ReCTS (task1).
The difference between our paper and ICDAR challenge is summarized here.

Updates

Aug 3, 2020: added guideline to use Baidu warpctc which reproduces CTC results of our paper.
Dec 27, 2019: added FLOPS in our paper, and minor updates such as log_dataset.txt and ICDAR2019-NormalizedED.
Oct 22, 2019: added confidence score, and arranged the output form of training logs.
Jul 31, 2019: The paper is accepted at International Conference on Computer Vision (ICCV), Seoul 2019, as an oral talk.
Jul 25, 2019: The code for floating-point 16 calculation, check @YacobBY's pull request
Jul 16, 2019: added ST_spe.zip dataset, word images contain special characters in SynthText (ST) dataset, see this issue
Jun 24, 2019: added gt.txt of failure cases that contains path and label of each image, see image_release_190624.zip
May 17, 2019: uploaded resources in Baidu Netdisk also, added Run demo. (check @sharavsambuu's colab demo also)
May 9, 2019: PyTorch version updated from 1.0.1 to 1.1.0, use torch.nn.CTCLoss instead of torch-baidu-ctc, and various minor updated.

Getting Started

Dependency

  • 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

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].

Run demo with pretrained model

  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

prediction results

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

Training and evaluation

  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
  1. 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
  1. 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

Arguments

  • --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.

Download failure cases and cleansed label from here

image_release.zip contains failure case images and benchmark evaluation images with cleansed label.

When you need to train on your own dataset or Non-Latin language datasets.

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

Acknowledgements

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

Reference

[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.

Links

Citation

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}
}

Contact

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

License

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.

Issues
  • To train on my own dataset

    To train on my own dataset

    Hi. I created lmdb dataset on my own data by running create_lmdb_dataset.py. then I run the train command on it and got the following output:

    CUDA_VISIBLE_DEVICES=0 python3 train.py --train_data result/train --valid_data result/test --Transformation TPS --FeatureExtraction ResNet --SequenceModeling BiLSTM --Prediction Attn

    dataset_root: result/train opt.select_data: ['MJ', 'ST'] opt.batch_ratio: ['0.5', '0.5']

    dataset_root: result/train dataset: MJ Traceback (most recent call last): File "train.py", line 283, in train(opt) File "train.py", line 26, in train train_dataset = Batch_Balanced_Dataset(opt) File "/home/mor-ai/Work/deep-text-recognition-benchmark/dataset.py", line 37, in init _dataset = hierarchical_dataset(root=opt.train_data, opt=opt, select_data=[selected_d]) File "/home/mor-ai/Work/deep-text-recognition-benchmark/dataset.py", line 106, in hierarchical_dataset concatenated_dataset = ConcatDataset(dataset_list) File "/home/mor-ai/.local/lib/python3.6/site-packages/torch/utils/data/dataset.py", line 187, in init assert len(datasets) > 0, 'datasets should not be an empty iterable' AssertionError: datasets should not be an empty iterable

    Can you help me resolve this?

    opened by xxxpsyduck 22
  • recog error using  TPS-ResNet、VGG-BiLSTM-Attn

    recog error using TPS-ResNet、VGG-BiLSTM-Attn

    The sample images d_autohomecar__wKgHPltYXyWAZHTbAANpvWtj5Hs964_0 和兰豪华感,而风上的 wrong d_autohomecar__wKgHPltYXyWAZHTbAANpvWtj5Hs964_1 内饰设局觉觉温馨范儿 wrong d_autohomecar__wKgHPlstHCiANf7JAAGvh7L-4DU249_1 后扭力梁非独立悬架 correct d_autohomecar__wKgHPlt2pX6AQNrVAANoQlFZZXQ045_0 变之水波落务变得更出 wrong

    I train the model using 32X256, then set batch_max_length=64(test and train),I feel something has wrong,when the character has many in the sample,the result is wrong。

    The traing datasets is normal。

    Thanks

    opened by AnddyWang 16
  • Accuracy difference between local retraining model and pretrained one

    Accuracy difference between local retraining model and pretrained one

    First, thanks for your great work :) ! You've done a good job!

    Here's my question, I've retrained the model with the option as: "--select_data MJ-ST --batch_ratio 0.5-0.5 --Transformation None --FeatureExtraction VGG --SequenceModeling BiLSTM --Prediction CTC" , corresponding to the original version of CRNN. The rest parameters are set as default and the model is trained on MJ and ST datasets.

    However, when testing with my local retrained best_accuracy model, the result accuracy is shown as below: in IC13_857: only 88.45% while 91.1% in paper. in IC13_1015: 87.68% while 89.2% in paper. in IC15_1811: 66.37% while 69.4% in paper. in IC15_2077: 64.07% while 64.2% in paper.

    It seems like there is still something inappropriate in my retraining process. Should I reset the learning rate or expand my training iteration? Do you guys have any idea about improving the performance to align with the public results illustrated in the paper?

    And I've attempted to train only on MJ dataset, whose model seems to have a higher accuracy in IC13_857. When I extend the training on both MJ and ST, is it necessary to add up the iteration number, so that I can get a better accuracy?

    Expect for your reply ^_^

    opened by 1LOVESJohnny 11
  • the best models  case insensitive?

    the best models case insensitive?

    the best models case insensitive?can you give case sensitive best models?

    opened by xiaoyubing 11
  • Training and testing on custom data

    Training and testing on custom data

    How can I train and test these models on my own data?

    opened by TsainGra 9
  • Inference code

    Inference code

    Hi, bravo for the great findings!

    Do you have any inference script?

    Thanks.

    opened by ghost 9
  • Can't training model with own lmdb dataset

    Can't training model with own lmdb dataset

    I have a problem training model with own lmdb dataset. I use create_lmdb_dataset.py with 1000 sample Vietnamese to create database. When I training model, dataset_root: data/training opt.select_data: ['ST'] opt.batch_ratio: ['0.5']

    dataset_root: data/training dataset: ST sub-directory: /ST num samples: 3 num total samples of ST: 3 x 1.0 (total_data_usage_ratio) = 3 num samples of ST per batch: 192 x 0.5 (batch_ratio) = 96

    Total_batch_size: 96 = 96

    Can you please tell me how to training own database. Thank you

    opened by thangtran480 8
  • validation wrong

    validation wrong

    when the code run at valid_loss, current_accuracy, current_norm_ED, preds, confidence_score, labels, infer_time, length_of_data = validation(model, criterion, valid_loader, converter, opt) , it stop here and raise no errors, Program cannot continue

    opened by cqray1990 8
  • Questions on datasets

    Questions on datasets "guideline"

    Hello guys,

    I would like to extract several words on several lines from simple images of approximately the same size.
    So I have to create my own Dataset! However, here are the questions I would like to answer with your help please:

    Q. For the text file (gt.txt): how can I write the "\n" to make a training dataset? The structure of the file does not seem to allow me to detect on several lines

    Q. The characters that can be detected are only alphanumeric. I need to recognize special characters (like : /,*,#, ...). Do I have to train the model for these characters too?

    Q. Your code to create a LMDB dataset for training can surely be used to create datasets for Test and for Validation?

    Thank you very much guys for your help!!!

    opened by GMXela 0
  • data_lmdb_release.zip in  dropbox download failure

    data_lmdb_release.zip in dropbox download failure

    it's too big ,can you split it or give a marginlink

    opened by freecells 0
  • does pretrained model not include space character?

    does pretrained model not include space character?

    hello everyone, i use TPS-ResNet-BiLSTM-Attn-case-sensitive.pth pretrained model, is this pretrained model include space character?

    opened by akbarwijayaa 0
  • the number of epochs or the number of batches.

    the number of epochs or the number of batches.

    When I look at the code, I found that the hyper parameter iter is not the number of epochs, but the number of batches. I understand right?

    opened by yusirhhh 0
  • Error in test.py says datasets should not be an empty iterable

    Error in test.py says datasets should not be an empty iterable

    My test.py keeps on saying that 'datasets should not be an empty iterable'. Can some please let me know what kind of folder structure and datatype should we pass as in test.py (notebook keeps throwing same error for image as well mdb)

    opened by ankalagigaurave 0
  • this project support space recognition?I add space in characters,but it does not work

    this project support space recognition?I add space in characters,but it does not work

    I want to use this project to recongnize space,but in fact I train dataset,I found the word image deep text recognition benchmark predict result is: deeptextrecognitionbenchmark so,space char can be support for this project?How can I do to recognize space

    opened by futureflsl 1
  • TypeError: cannot pickle 'Environment' object

    TypeError: cannot pickle 'Environment' object

    Hello,

    I have this error and I really don't know what to do. I'm new in coding so, I really need help! :-P

    there is all lines in my Terminal : (THX for helping me)

    PS C:\Users\guit_\PycharmProjects\Text Recognition\deep-text-recognition-benchmark-master> python 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 ResNet --SequenceModeling BiLSTM --Prediction CTC Filtering the images containing characters which are not in opt.character Filtering the images whose label is longer than opt.batch_max_length

    dataset_root: data_lmdb_release/training opt.select_data: ['MJ', 'ST'] opt.batch_ratio: ['0.5', '0.5']

    dataset_root: data_lmdb_release/training dataset: MJ sub-directory: /MJ\MJ_test num samples: 891924 sub-directory: /MJ\MJ_train num samples: 7224586 sub-directory: /MJ\MJ_valid num samples: 802731 num total samples of MJ: 8919241 x 1.0 (total_data_usage_ratio) = 8919241 num samples of MJ per batch: 192 x 0.5 (batch_ratio) = 96 Traceback (most recent call last): File "train.py", line 317, in train(opt) File "train.py", line 31, in train train_dataset = Batch_Balanced_Dataset(opt) File "C:\Users\guit_\PycharmProjects\Text Recognition\deep-text-recognition-benchmark-master\dataset.py", line 69, in init self.dataloader_iter_list.append(iter(data_loader)) File "C:\Users\guit.conda\envs\Benchmark_env\lib\site-packages\torch\utils\data\dataloader.py", line 359, in iter return self.get_iterator() File "C:\Users\guit.conda\envs\Benchmark_env\lib\site-packages\torch\utils\data\dataloader.py", line 305, in get_iterator return MultiProcessingDataLoaderIter(self) File "C:\Users\guit.conda\envs\Benchmark_env\lib\site-packages\torch\utils\data\dataloader.py", line 918, in init w.start() File "C:\Users\guit.conda\envs\Benchmark_env\lib\multiprocessing\process.py", line 121, in start self.popen = self.Popen(self) File "C:\Users\guit.conda\envs\Benchmark_env\lib\multiprocessing\context.py", line 224, in Popen return default_context.get_context().Process.Popen(process_obj) File "C:\Users\guit.conda\envs\Benchmark_env\lib\multiprocessing\context.py", line 327, in Popen return Popen(process_obj) File "C:\Users\guit.conda\envs\Benchmark_env\lib\multiprocessing\popen_spawn_win32.py", line 93, in init reduction.dump(process_obj, to_child) File "C:\Users\guit.conda\envs\Benchmark_env\lib\multiprocessing\reduction.py", line 60, in dump ForkingPickler(file, protocol).dump(obj) TypeError: cannot pickle 'Environment' object PS C:\Users\guit\PycharmProjects\Text Recognition\deep-text-recognition-benchmark-master> Traceback (most recent call last): File "", line 1, in File "C:\Users\guit.conda\envs\Benchmark_env\lib\multiprocessing\spawn.py", line 116, in spawn_main exitcode = main(fd, parent_sentinel) File "C:\Users\guit.conda\envs\Benchmark_env\lib\multiprocessing\spawn.py", line 126, in _main self = reduction.pickle.load(from_parent) EOFError: Ran out of input

    opened by GMXela 2
  • support training progress bar

    support training progress bar

    Support of displaying a progress bar while training

    opened by bakrianoo 0
  • set new parameter to save the model to a specific path

    set new parameter to save the model to a specific path

    Support of specifying the path where the model will be saved, rather than saving it to a relative path.

    opened by bakrianoo 0
  • cost is nan

    cost is nan

    what is the problem about cost is nan?

    opened by LinXin04 0
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