algorithm_rec_visionlan_en.md

VisionLAN

• 1. Introduction
• 2. Environment
• 3. Model Training / Evaluation / Prediction
  • 3.1 Training
  • 3.2 Evaluation
  • 3.3 Prediction
• 4. Inference and Deployment
  • 4.1 Python Inference
  • 4.2 C++ Inference
  • 4.3 Serving
  • 4.4 More
• 5. FAQ

1. Introduction

Paper:

From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network Yuxin Wang, Hongtao Xie, Shancheng Fang, Jing Wang, Shenggao Zhu, Yongdong Zhang ICCV, 2021

Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:

Model	Backbone	config	Acc	Download link
VisionLAN	ResNet45	rec_r45_visionlan.yml	90.3%	预训练、训练模型

2. Environment

Please refer to "Environment Preparation" to configure the PaddleOCR environment, and refer to "Project Clone" to clone the project code.

3. Model Training / Evaluation / Prediction

Please refer to Text Recognition Tutorial. PaddleOCR modularizes the code, and training different recognition models only requires changing the configuration file.

Training:

Specifically, after the data preparation is completed, the training can be started. The training command is as follows:

#Single GPU training (long training period, not recommended)
python3 tools/train.py -c configs/rec/rec_r45_visionlan.yml

#Multi GPU training, specify the gpu number through the --gpus parameter
python3 -m paddle.distributed.launch --gpus '0,1,2,3'  tools/train.py -c configs/rec/rec_r45_visionlan.yml

Evaluation:

# GPU evaluation
python3 tools/eval.py -c configs/rec/rec_r45_visionlan.yml -o Global.pretrained_model={path/to/weights}/best_accuracy

Prediction:

# The configuration file used for prediction must match the training
python3 tools/infer_rec.py -c configs/rec/rec_r45_visionlan.yml -o Global.infer_img='./doc/imgs_words/en/word_2.png' Global.pretrained_model=./rec_r45_visionlan_train/best_accuracy

4. Inference and Deployment

4.1 Python Inference

First, the model saved during the VisionLAN text recognition training process is converted into an inference model. ( Model download link) ), you can use the following command to convert:

python3 tools/export_model.py -c configs/rec/rec_r45_visionlan.yml -o Global.pretrained_model=./rec_r45_visionlan_train/best_accuracy Global.save_inference_dir=./inference/rec_r45_visionlan/

Note:

• If you are training the model on your own dataset and have modified the dictionary file, please pay attention to modify the character_dict_path in the configuration file to the modified dictionary file.
• If you modified the input size during training, please modify the infer_shape corresponding to VisionLAN in the tools/export_model.py file.

After the conversion is successful, there are three files in the directory:

./inference/rec_r45_visionlan/
    ├── inference.pdiparams
    ├── inference.pdiparams.info
    └── inference.pdmodel

For VisionLAN text recognition model inference, the following commands can be executed:

python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words/en/word_2.png' --rec_model_dir='./inference/rec_r45_visionlan/' --rec_algorithm='VisionLAN' --rec_image_shape='3,64,256' --rec_char_dict_path='./ppocr/utils/ic15_dict.txt' --use_space_char=False

After executing the command, the prediction result (recognized text and score) of the image above is printed to the screen, an example is as follows: The result is as follows:

Predicts of ./doc/imgs_words/en/word_2.png:('yourself', 0.9999493)

4.2 C++ Inference

Not supported

4.3 Serving

Not supported

4.4 More

Not supported

5. FAQ

1. Note that the MJSynth and SynthText datasets come from VisionLAN repo.
2. We use the pre-trained model provided by the VisionLAN authors for finetune training.

Citation

@inproceedings{wang2021two,
  title={From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network},
  author={Wang, Yuxin and Xie, Hongtao and Fang, Shancheng and Wang, Jing and Zhu, Shenggao and Zhang, Yongdong},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={14194--14203},
  year={2021}
}