128 lines
3.7 KiB
Markdown
128 lines
3.7 KiB
Markdown
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# Text Gestalt
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- [1. 算法简介](#1)
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- [2. 环境配置](#2)
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- [3. 模型训练、评估、预测](#3)
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- [3.1 训练](#3-1)
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- [3.2 评估](#3-2)
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- [3.3 预测](#3-3)
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- [4. 推理部署](#4)
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- [4.1 Python推理](#4-1)
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- [4.2 C++推理](#4-2)
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- [4.3 Serving服务化部署](#4-3)
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- [4.4 更多推理部署](#4-4)
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- [5. FAQ](#5)
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<a name="1"></a>
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## 1. 算法简介
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论文信息：
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> [Text Gestalt: Stroke-Aware Scene Text Image Super-Resolution](https://arxiv.org/pdf/2112.08171.pdf)
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> Chen, Jingye and Yu, Haiyang and Ma, Jianqi and Li, Bin and Xue, Xiangyang
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> AAAI, 2022
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参考[FudanOCR](https://github.com/FudanVI/FudanOCR/tree/main/text-gestalt) 数据下载说明，在TextZoom测试集合上超分算法效果如下：
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|模型|骨干网络|PSNR_Avg|SSIM_Avg|配置文件|下载链接|
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|---|---|---|---|---|---|
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|Text Gestalt|tsrn|19.28|0.6560| [configs/sr/sr_tsrn_transformer_strock.yml](../../configs/sr/sr_tsrn_transformer_strock.yml)|[训练模型](https://paddleocr.bj.bcebos.com/sr_tsrn_transformer_strock_train.tar)|
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<a name="2"></a>
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## 2. 环境配置
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请先参考[《运行环境准备》](./environment.md)配置PaddleOCR运行环境，参考[《项目克隆》](./clone.md)克隆项目代码。
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<a name="3"></a>
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## 3. 模型训练、评估、预测
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请参考[文本识别训练教程](./recognition.md)。PaddleOCR对代码进行了模块化，训练不同的识别模型只需要**更换配置文件**即可。
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- 训练
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在完成数据准备后，便可以启动训练，训练命令如下：
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```
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#单卡训练（训练周期长，不建议）
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python3 tools/train.py -c configs/sr/sr_tsrn_transformer_strock.yml
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#多卡训练，通过--gpus参数指定卡号
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python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/sr/sr_tsrn_transformer_strock.yml
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```
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- 评估
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```
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# GPU 评估， Global.pretrained_model 为待测权重
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python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/sr/sr_tsrn_transformer_strock.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
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```
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- 预测：
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```
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# 预测使用的配置文件必须与训练一致
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python3 tools/infer_sr.py -c configs/sr/sr_tsrn_transformer_strock.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words_en/word_52.png
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```
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执行命令后，上面图像的超分结果如下：
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<a name="4"></a>
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## 4. 推理部署
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<a name="4-1"></a>
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### 4.1 Python推理
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首先将文本超分训练过程中保存的模型，转换成inference model。以 Text-Gestalt 训练的[模型](https://paddleocr.bj.bcebos.com/sr_tsrn_transformer_strock_train.tar) 为例，可以使用如下命令进行转换：
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```shell
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python3 tools/export_model.py -c configs/sr/sr_tsrn_transformer_strock.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.save_inference_dir=./inference/sr_out
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```
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Text-Gestalt 文本超分模型推理，可以执行如下命令：
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```
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python3 tools/infer/predict_sr.py --sr_model_dir=./inference/sr_out --image_dir=doc/imgs_words_en/word_52.png --sr_image_shape=3,32,128
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```
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执行命令后，图像的超分结果如下：
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<a name="4-2"></a>
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### 4.2 C++推理
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暂未支持
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<a name="4-3"></a>
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### 4.3 Serving服务化部署
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暂未支持
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<a name="4-4"></a>
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### 4.4 更多推理部署
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暂未支持
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<a name="5"></a>
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## 5. FAQ
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## 引用
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```bibtex
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@inproceedings{chen2022text,
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title={Text gestalt: Stroke-aware scene text image super-resolution},
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author={Chen, Jingye and Yu, Haiyang and Ma, Jianqi and Li, Bin and Xue, Xiangyang},
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booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
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volume={36},
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number={1},
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pages={285--293},
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year={2022}
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}
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```
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