mango

MANGO

This code repository contains the implementations of the paper MANGO: A Mask Attention Guided One-Stage Scene Text Spotter (AAAI 2021).

Preparing Dataset

Original images can be downloaded from: Total-Text , ICDAR2013 , ICDAR2015, ICDAR2017_MLT, ICDAR2019_MLT

The formatted training datalist can be found in demo/text_spotting/datalist

Train On Your Own Dataset

1.Firstly, download the pre-trained model, which was well trained on SynthText and SynthText_Curve).

2.Modified the paths (ann_file, img_prefix, work_dir, etc..) in the config files demo/text_spotting/mango/config/mango_r50_ete_finetune_ic13.py.

3.Run the following bash command in the command line

>>> cd $DAVAR_LAB_OCR_ROOT$/demo/text_spotting/mango/
>>> bash dist_train.sh

Notice:We provide the implementation of online validation. If you want to close it to save training time, you may modify the startup script to add --no-validate command.

Train From Scratch

If you want to re-implement the model's performance from scratch, please following these steps:

1.Firstly, pre-train the attention module using the SynthText containing character-level annotations. See demo/text_spotting/mango/configs/mango_r50_att_pretrain.py for more details.

2.Secondly, end-to-end training using the SynthText and SynthCurve containing only word-level annotations. See demo/text_spotting/mango/configs/mango_r50_ete_pretrain.py for more details.

Notice:At the beginning of training, attention module and recognition module are trained together to prevent attention module from collapsing. The pretrained model is provided as mentioned above.

3.Thirdly, Fine-tune model on the mixed real dataset (include:ICADR2013~2019, Total-Text). See demo/text_spotting/mango/configs/mango_r50_ete_finetune_ic13.py for more details.

4.Finally, Fine-tune on the ICDAR2013, ICDAR2015 and Total-Text separately for testing and evaluation.

Notice:Fine-tune on the ICDAR2015 with num_gird=60, and on the ICDAR2013 and Total-Text with num_grid=40

Offline Inference and Evaluation

We provide a demo of forward inference and evaluation. You can modify the parameter (iou_constraint, lexicon_type, etc..) in the testing script, and start testing:

>>> cd $DAVAR_LAB_OCR_ROOT$/demo/text_spotting/mango/tools/
>>> bash test_ic13.sh

The offline evaluation tool can be found in davarocr/demo/text_spotting/evaluation/.

Visualization

We provide a script to visualize the intermediate output results of the model, include visualization results of segmentation, activated grid map, text pred and attention map. You can modify the paths (test_dataset, config_file, etc..) in the script, and start generating visualization results:

>>> cd $DAVAR_LAB_OCR_ROOT$/demo/text_spotting/mango/tools/
>>> python vis.py

Some visualization results are shown:

Trained Model Download

All of the models are re-implemented and well trained in the based on the opensourced framework mmdetection. So, the results might be slightly different from reported results.

Results on various datasets and trained models download:

Pipeline	Pretrained-Dataset	Links
resnet50+fpn+CMA+lstm	SynthText SynthCurve	cfg , pth (Access Code: rxNp)
resnet101+fpn+CMA+lstm	SynthText SynthCurve	cfg , pth (Access Code: 9aX7)

Dataset	Backbone	Pretrained	Mix-Finetune	Specific-Finetune	Test Scale	End-to-End			Word Spotting			Links
						General	Weak	Strong	General	Weak	Strong
ICDAR2013 (Reported)	ResNet-50	SynthText SynthCurve	ICDAR2013 ICDAR2015 ICDAR2017_MLT COCO-Text Total-Text	None	L-1440	86.9	90.0	90.5	90.1	94.1	94.8	-
ICDAR2013	ResNet-50	SynthText SynthCurve	ICDAR2013 ICDAR2015 ICDAR2017_MLT ICDAR2019_MLT Total-Text	None	L-1440	84.9	88.6	89.5	88.4	92.7	93.7	cfg , pth (Access Code: UJKZ)
ICDAR2013	ResNet-101	SynthText SynthCurve	ICDAR2013 ICDAR2015 ICDAR2017_MLT ICDAR2019_MLT Total-Text	None	L-1440	88	90.3	90.4	90.7	93.8	94.0	cfg , pth (Access Code: hn63)
ICDAR2015 (Reported)	ResNet-50	SynthText SynthCurve	ICDAR2013 ICDAR2015 ICDAR2017_MLT COCO-Text Total-Text	ICDAR2015	L-1800	67.3	78.9	81.8	70.3	83.1	86.4	-
ICDAR2015	ResNet-50	SynthText SynthCurve	ICDAR2013 ICDAR2015 ICDAR2017_MLT ICDAR2019_MLT Total-Text	ICDAR2015	L-1800	70.8	77.4	80.7	73.8	81.1	85	cfg , pth (Access Code: 7nMl)
ICDAR2015	ResNet-101	SynthText SynthCurve	ICDAR2013 ICDAR2015 ICDAR2017_MLT ICDAR2019_MLT Total-Text	ICDAR2015	L-1800	72.8	79.8	82.4	75.7	83.4	86.6	cfg , pth (Access Code: eHT3)

Dataset	Backbone	Pretrained	Mix-Finetune	Specific-Finetune	Test Scale	End-to-End		Word Spotting		Links
						None	Full	None	Full
Total-Text (Reported)	ResNet-50	SynthText SynthCurve	ICDAR2013 ICDAR2015 ICDAR2017_MLT COCO-Text Total-Text	Total-Text	L-1600	-	-	72.9	83.6	-
Total-Text	ResNet-50	SynthText SynthCurve	ICDAR2013 ICDAR2015 ICDAR2017_MLT ICDAR2019_MLT Total-Text	Total-Text	L-1600	68.9	78.9	71.7	82.7	cfg , pth (Access Code: F6cX)
Total-Text	ResNet-101	SynthText SynthCurve	ICDAR2013 ICDAR2015 ICDAR2017_MLT ICDAR2019_MLT Total-Text	Total-Text	L-1600	70.2	79.9	73	83.9	cfg , pth (Access Code: eosh)

Citation

If you find this repository is helpful to your research, please feel free to cite us:

@inproceedings{qiao2021mango,
  title={MANGO: A Mask Attention Guided One-Stage Scene Text Spotter},
  author={Qiao, Liang and Chen, Ying and Cheng, Zhanzhan and Xu, Yunlu and Niu, Yi and Pu, Shiliang and Wu, Fei},
  booktitle={Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI)},
  pages={2467-2476},
  year={2021}
}

License

This project is released under the Apache 2.0 license

Copyright

If there is any suggestion and problem, please feel free to contact the author with [email protected] or [email protected].