yoro

YORO

This code repository contains the implementations of the paper You Only Recognize Once: Towards Fast Video Text Spotting. (ACM MM 2019)

Preparing Dataset

Datasets used in experiments can be downloaded from: Text in Videos ICDAR2013/ICDAR2015.

For accessing the LSVTD (the Large Scene Video Text Dataset), You can refer to : LSVTD

The formatted training datalist and test datalist can be found in demo/videotext/datalist/

Training

1. Detection Training

   1. We first train the pre-train model of text detection branch, using the detector of EAST, on dataset "Text in Videos ICDAR2015" and "Incidental Scene Text ICDAR2015"

      Modify the paths ("ann_file"/ "img_prefix"/ "work_dir", etc.) in the config files demo/videotext/yoro/det/config/east_r50_rbox.py.

      Run the following bash command in the command line,

      cd $DAVAR_LAB_OCR_ROOT$/demo/videotext/yoro/det/
      bash base_dist_train.sh

      trained model can be downloaded in pth (Access code: tk6b)

   2. Extract optical flow information, using TVL1 algorithm in opencv2

      Modify the paths ("path"/ "output_path"/, etc.) in the script demo/videotext/yoro/scripts/extract_flow.py

      Run the following bash command in the command line,

      cd $DAVAR_LAB_OCR_ROOT$/demo/videotext/yoro/scripts/
      python extract_flow.py

   3. Fine-tune the Spatial-temporal detector on dataset "Text in Videos ICDAR2015"

      Modify the paths ("ann_file"/ "img_prefix"/ "work_dir", etc.) in the config files demo/videotext/yoro/det/config/yoro_east.py.

      Run the following bash command in the command line,

      cd $DAVAR_LAB_OCR_ROOT$/demo/videotext/yoro/det/
      bash dist_train.sh

      trained models in IC13-ViT and IC15-ViT can be downloaded in pth (Access code: 19fc)

2. Text Recommender Training

   1. We first only train the recognition branch.

      Modify the paths ("ann_file"/ "img_prefix"/ "work_dir", etc.) in the config files demo/videotext/yoro/rcg/config/ic15_rgb_res32_bilstm_attn.py.

      Run the following bash command in the command line,

      cd $DAVAR_LAB_OCR_ROOT$/demo/videotext/yoro/rcg/
      bash train_rcg.sh

      trained model can be downloaded in pth (Access code: 062k)

   2. Fix the recognition branch and only train the track branch.

      Modify the paths ("work_dir"/ "load_from"/, etc.) in the config files demo/videotext/yoro/rcg/config/ic15_track_rgb_res32_bilstm_attn.py.

      Run the following bash command in the command line,

      cd $DAVAR_LAB_OCR_ROOT$/demo/videotext/yoro/rcg/
      bash track_train.sh

      trained model can be downloaded in pth (Access code: pdq7)

   3. Generate gt quality scores for train data list.

      Modify the paths ("out_path/"AnnFile"/ "ModelPath"/, etc.) in the config files demo/videotext/yoro/rcg/test_scripts/config/config_gt_score.py.

      Run the following bash command in the command line,

      cd $DAVAR_LAB_OCR_ROOT$/demo/videotext/yoro/rcg/test_scripts/
      bash gen_train_score.sh

   4. Finally, fix the recognition and track branch, only train the quality score branch

      Modify the paths ("ann_files/"work_dir"/ "load_from"/, etc.) in the config files demo/videotext/yoro/rcg/config/ic15_qscore_rgb_res32_bilstm_attn.py.

      Run the following bash command in the command line,

      cd $DAVAR_LAB_OCR_ROOT$/demo/videotext/yoro/rcg/
      bash qscore_train.sh

      trained model can be downloaded in pth (Access code: 37r2)

Offline Inference and Evaluation

1. Detection Test

   Modify the paths ("test_dataset"/ "image_prefix"/ "flow_path", etc.) in the config files demo/videotext/yoro/det/test.py.

   python test.py 

   Some visualization of detection results are shown:

   

2. End-to-End Spotting Test

   Modify the paths ("AnnFile"/ "out_dir"/ "ModelPath", etc.) in the config files demo/videotext/yoro/rcg/test_scripts/config/test_spotting_config.py.

   Run the following bash command in the command line,

   cd $DAVAR_LAB_OCR_ROOT$/demo/videotext/yoro/rcg/test_scripts/
   bash test_spotting.sh

   You can use our postprocessing methor to merge the broken seqs:

   Run the following bash command in the command line,

   cd $DAVAR_LAB_OCR_ROOT$/davarocr/davar_videotext/tools/
   python merge_seq.py &input_file &output_file

Offline Evaluation

1. Detection Evaluation The general evaluation tool can be found in davarocr/demo/text_detection/evaluation/.

2. Spotting Evaluation The general evaluation tool can be found in davarocr/demo/videotext/evaluation/.

Trained Model Download

All of the models are re-implemented and well trained in the based on the opensourced framework mmdetection.

Results on various datasets and trained models download, Notice that we don't use RandomCrop pre-processing operation due to the simplification, So the results may have a dither compared with results in the paper:

Notice: Text in Video is shorten as TiV. Incidental Scene Text is shorten as IST.

1.Detection Results

Dataset	Mode	Backbone	Pre-trained	Test Scale	Precision	Recall	Hmean	Links
ICDAR2013 TiV (Reported)	RBOX	ResNet-50	ICDAR2015 IST, Coco-Text	(L-1280, S-720)	81.45	62.23	69.25	-
ICDAR2013 TiV	RBOX	ResNet-50	ICDAR2015 TiV, ICDAR2015IST	(L-1280, S-720)	78.96	61.78	69.32	config, pth (Access Code: 19fc)
ICDAR2015 TiV	RBOX	ResNet-50	ICDAR2015 TiV, ICDAR2015IST	(L-1280, S-720)	79.55	64.45	71.21	config, pth (Access Code: 19fc)

2.Text Recommender Results

Dataset	Test Scale	ATA	MOTA	MOTP	Precision-R	Recall-R	F-score	ATA-R	MOTA-R	MOTP-R	Links
ICDAR2015 TiV (Reported)	(L-100, S-32)	0.65	0.68	0.76	68.28	67.21	67.74	0.63	0.69	0.76	-
ICDAR2015 TiV	(L-100, S-32)	0.65	0.71	0.74	71.13	68.88	69.50	0.63	0.68	0.74	config, pth (Access code: 37r2)

Note: Above links are from BaiduYunPan. Models can also be downloaded from Google Drive.

Citation

@inproceedings{cheng2019you,
  title={You only recognize once: Towards fast video text spotting},
  author={Cheng, Zhanzhan and Lu, Jing and Niu, Yi and Pu, Shiliang and Wu, Fei and Zhou, Shuigeng},
  booktitle={Proceedings of the 27th ACM International Conference on Multimedia},
  pages={855--863},
  year={2019}
}

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 zoubaorui@hikvision.com or chengzhanzhan@hikvision.com.