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77 lines
3.2 KiB
Markdown
77 lines
3.2 KiB
Markdown
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---
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typora-copy-images-to: images
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comments: true
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---
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# EAST
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## 1. Introduction
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Paper:
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> [EAST: An Efficient and Accurate Scene Text Detector](https://arxiv.org/abs/1704.03155)
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> Xinyu Zhou, Cong Yao, He Wen, Yuzhi Wang, Shuchang Zhou, Weiran He, Jiajun Liang
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> CVPR, 2017
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On the ICDAR2015 dataset, the text detection result is as follows:
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|Model|Backbone|Configuration|Precision|Recall|Hmean|Download|
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| --- | --- | --- | --- | --- | --- | --- |
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|EAST|ResNet50_vd| [det_r50_vd_east.yml](../../configs/det/det_r50_vd_east.yml)|88.71%| 81.36%| 84.88%| [model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)|
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|EAST|MobileNetV3|[det_mv3_east.yml](../../configs/det/det_mv3_east.yml) | 78.20%| 79.10%| 78.65%| [model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_east_v2.0_train.tar)|
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## 2. Environment
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Please prepare your environment referring to [prepare the environment](../../ppocr/environment.en.md) and [clone the repo](../../ppocr/blog/clone.en.md).
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## 3. Model Training / Evaluation / Prediction
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The above EAST model is trained using the ICDAR2015 text detection public dataset. For the download of the dataset, please refer to [ocr_datasets](./dataset/ocr_datasets_en.md).
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After the data download is complete, please refer to [Text Detection Training Tutorial](../../ppocr/model_train/detection.en.md) for training. PaddleOCR has modularized the code structure, so that you only need to **replace the configuration file** to train different detection models.
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## 4. Inference and Deployment
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### 4.1 Python Inference
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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](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)), you can use the following command to convert:
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```bash linenums="1"
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python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.pretrained_model=./det_r50_vd_east_v2.0_train/best_accuracy Global.save_inference_dir=./inference/det_r50_east/
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```
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For EAST text detection model inference, you need to set the parameter --det_algorithm="EAST", run the following command:
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```bash linenums="1"
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python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_r50_east/" --det_algorithm="EAST"
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```
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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`.
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### 4.2 C++ Inference
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Since the post-processing is not written in CPP, the EAST text detection model does not support CPP inference.
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### 4.3 Serving
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Not supported
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### 4.4 More
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Not supported
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## 5. FAQ
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## Citation
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```bibtex
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@inproceedings{zhou2017east,
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title={East: an efficient and accurate scene text detector},
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author={Zhou, Xinyu and Yao, Cong and Wen, He and Wang, Yuzhi and Zhou, Shuchang and He, Weiran and Liang, Jiajun},
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booktitle={Proceedings of the IEEE conference on Computer Vision and Pattern Recognition},
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pages={5551--5560},
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year={2017}
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}
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```
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