EAST¶
1. Introduction¶
Paper:
EAST: An Efficient and Accurate Scene Text Detector Xinyu Zhou, Cong Yao, He Wen, Yuzhi Wang, Shuchang Zhou, Weiran He, Jiajun Liang CVPR, 2017
On the ICDAR2015 dataset, the text detection result is as follows:
Model | Backbone | Configuration | Precision | Recall | Hmean | Download |
---|---|---|---|---|---|---|
EAST | ResNet50_vd | det_r50_vd_east.yml | 88.71% | 81.36% | 84.88% | model |
EAST | MobileNetV3 | det_mv3_east.yml | 78.20% | 79.10% | 78.65% | model |
2. Environment¶
Please prepare your environment referring to prepare the environment and clone the repo.
3. Model Training / Evaluation / Prediction¶
The above EAST model is trained using the ICDAR2015 text detection public dataset. For the download of the dataset, please refer to ocr_datasets.
After the data download is complete, please refer to Text Detection Training Tutorial for training. PaddleOCR has modularized the code structure, so that you only need to replace the configuration file to train different detection models.
4. Inference and Deployment¶
4.1 Python Inference¶
First, convert the model saved in the EAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as example (model download link), you can use the following command to convert:
For EAST text detection model inference, you need to set the parameter --det_algorithm="EAST", run the following command:
The visualized text detection results are saved to the ./inference_results
folder by default, and the name of the result file is prefixed with det_res
.
4.2 C++ Inference¶
Since the post-processing is not written in CPP, the EAST text detection model does not support CPP inference.
4.3 Serving¶
Not supported
4.4 More¶
Not supported
5. FAQ¶
Citation¶
@inproceedings{zhou2017east,
title={East: an efficient and accurate scene text detector},
author={Zhou, Xinyu and Yao, Cong and Wen, He and Wang, Yuzhi and Zhou, Shuchang and He, Weiran and Liang, Jiajun},
booktitle={Proceedings of the IEEE conference on Computer Vision and Pattern Recognition},
pages={5551--5560},
year={2017}
}