GeneralOCR is open source Optical Character Recognition based on PyTorch. It makes a fidelity and useful tool to implement SOTA models on OCR domain. You can use them to infer and train the model with your customized dataset. The solution architecture of this project is re-implemented from facebook Detectron and openmm-cv.
Refer to the guideline of gen_ocr installation
Supported Algorithms:
Text Detection
| Algorithm | Paper | Python argument (--det) |
|---|---|---|
| - [x] DBNet (AAAI'2020) | https://arxiv.org/pdf/1911.08947 | DB_r18, DB_r50 |
| - [x] Mask R-CNN (ICCV'2017) | https://arxiv.org/abs/1703.06870 | MaskRCNN_CTW, MaskRCNN_IC15, MaskRCNN_IC17 |
| - [x] PANet (ICCV'2019) | https://arxiv.org/abs/1908.06391 | PANet_CTW, PANet_IC15 |
| - [x] PSENet (CVPR'2019) | https://arxiv.org/abs/1903.12473 | PS_CTW, PS_IC15 |
| - [x] TextSnake (ECCV'2018) | https://arxiv.org/abs/1807.01544 | TextSnake |
| - [x] DRRG (CVPR'2020) | https://arxiv.org/abs/2003.07493 | DRRG |
| - [x] FCENet (CVPR'2021) | https://arxiv.org/abs/2104.10442 | FCE_IC15, FCE_CTW_DCNv2 |
Table 1: Text detection algorithms, papers and arguments configuration in package.
Text Recognition
| Algorithm | Paper | Python argument (--recog) |
|---|---|---|
| - [x] CRNN (TPAMI'2016) | https://arxiv.org/abs/1507.05717 | CRNN, CRNN_TPS |
| - [x] NRTR (ICDAR'2019) | https://arxiv.org/abs/1806.00926 | NRTR_1/8-1/4, NRTR_1/16-1/8 |
| - [x] RobustScanner (ECCV'2020) | https://arxiv.org/abs/2007.07542 | RobustScanner |
| - [x] SAR (AAAI'2019) | https://arxiv.org/abs/1811.00751 | SAR |
| - [x] SATRN (CVPR'2020 Workshop on Text and Documents in the Deep Learning Era) | https://arxiv.org/abs/1910.04396 | SATRN, SATRN_sm |
| - [x] SegOCR (Manuscript'2021) | - | SEG |
Table 2: Text recognition algorithms, papers and arguments configuration in package.
# Activate your conda environment
conda activate gen_ocr
python general_ocr/utils/ocr.py demo/demo_text_ocr_2.jpg --print-result --imshow --det TextSnake --recog SEG--det and --recog argument values are supplied in table 1 and table 2.
The result as below:
We prepare toy datasets for you to train on /tests/data folder in which you can do your experiment before training with the official datasets.
python tools/train.py configs/textrecog/robust_scanner/seg_r31_1by16_fpnocr_toy_dataset.py --work-dir seg
To change text recognition algorithm into sag:
python tools/train.py configs/textrecog/sar/sar_r31_parallel_decoder_toy_dataset.py --work-dir sar
When you train Academic dataset, you need to setup dataset directory as this guideline. The main point you should forecus is that your model point to the right dataset directory. Assume that you want to train model TextSnake on CTW1500 dataset, thus your config file of that model in configs/textdet/textsnake/textsnake_r50_fpn_unet_1200e_ctw1500.py should be as below:
dataset_type = 'IcdarDataset'
data_root = 'data/ctw1500/'
data = dict(
samples_per_gpu=4,
workers_per_gpu=4,
val_dataloader=dict(samples_per_gpu=1),
test_dataloader=dict(samples_per_gpu=1),
train=dict(
type=dataset_type,
ann_file=f'{data_root}/instances_training.json',
img_prefix=f'{data_root}/imgs',
pipeline=train_pipeline),
val=dict(
type=dataset_type,
ann_file=f'{data_root}/instances_test.json',
img_prefix=f'{data_root}/imgs',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file=f'{data_root}/instances_test.json',
img_prefix=f'{data_root}/imgs',
pipeline=test_pipeline))
Your data_root folder data/ctw1500/ have to be right. Afterward, train your model:
python ./tools/train.py configs/textdet/textsnake/textsnake_r50_fpn_unet_1200e_ctw1500.py --work-dir textsnakeTo study other configuration parameters on training.
Now you completed training of TextSnake and get the checkpoint textsnake/lastest.pth. You should evaluate peformance on test set using hmean-iou metric:
python tools/test.py configs/textdet/textsnake/textsnake_r50_fpn_unet_1200e_ctw1500.py textsnake/latest.pth --eval hmean-iou-
Design pattern: Each type of object such as
dataset, encoder, decoder, backbone, layer, model, lossis registered according to group and scope context coming from facebook-detectron. -
Layers: Layers architecture and pretrained-models is mainly refered from open-mmcv.
- Implements: Paddle-OCR, TR-OCR, TessaractOCR.
If you find this project is useful in your reasearch, kindly consider cite:
@article{genearal_ocr,
title={GeneralOCR: A Comprehensive package for OCR models},
author={khanhphamdinh},
email= {phamdinhkhanh.tkt53.neu@gmail.com},
year={2021}
}