hanquansanren / Consistency_Regularization_STR

It's the code for the paper Pushing the Performance Limit of Scene Text Recognizer without Human Annotation, CVPR 2022.

Consistency_Regularization_STR

It's the code for the paper Pushing the Performance Limit of Scene Text Recognizer without Human Annotation, CVPR 2022. Test in Python3.7.

Install the enviroment

    pip install -r requirements.txt

Data Prepare

Please convert your own dataset to LMDB format by create_dataset.py. (Borrowed from https://github.com/bgshih/crnn/blob/master/tool/create_dataset.py, provided by Baoguang Shi)

For labeled dataset, there are converted Synth90K and SynthText LMDB dataset by luyang-NWPU: [Here], password: tw3x

For unlabeled dataset, you could download raw images from imagenet, places2 and openimages, then detect and crop word images from these images.

Supervised Training

sh run_baseline.sh

Semi-Supervised Training

sh run_semi.sh

Testing

sh run_test.sh

Pretrained Models and Results

download pretrained model from here(https://pan.baidu.com/s/1JF97VY0oiPiK5GpDEsYioQ?pwd=abhr, password:abhr) and put them in saved_models.

Model	Labeled data	Unlabeled data	IC13 857	IC13 1015	SVT	IIIT	IC15 1811	IC15 2077	SVTP	CUTE
TRBA_pr	10% (Synth90K+SynthText)	-	96.3	94.3	91.5	94.3	81.5	77.7	84.2	87.5
TRBA_cr	10% (Synth90K+SynthText)	1.06M unlabeled real data	97.2	95.9	94.3	96.6	86.8	79.7	89.0	93.4
TRBA_pr	Synth90K + SynthText	-	97.2	95.9	91.8	95.5	83.6	79.7	87.3	88.5
TRBA_cr	Synth90K + SynthText	10.6M unlabeled real data	98.0	96.4	96.0	97.0	88.8	84.9	90.9	95.1

Acknowledgment

This code is based on STR-Fewer-Labels by Jeonghun Baek. Thanks for your contribution.