2022-04-28 02:28:14 +00:00
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# SAR
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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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> [Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition](https://arxiv.org/abs/1811.00751)
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> Hui Li, Peng Wang, Chunhua Shen, Guyu Zhang
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> AAAI, 2019
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使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法复现效果如下：
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|模型|骨干网络|配置文件|Acc|下载链接|
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2022-04-28 11:23:53 +00:00
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| --- | --- | --- | --- | --- |
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2022-04-28 02:28:14 +00:00
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|SAR|ResNet31|[rec_r31_sar.yml](../../configs/rec/rec_r31_sar.yml)|87.20%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_r31_sar_train.tar)|
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注：除了使用MJSynth和SynthText两个文字识别数据集外，还加入了[SynthAdd](https://pan.baidu.com/share/init?surl=uV0LtoNmcxbO-0YA7Ch4dg)数据（提取码：627x），和部分真实数据，具体数据细节可以参考论文。
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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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具体地，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/rec/rec_r31_sar.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/rec/rec_r31_sar.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/rec/rec_r31_sar.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_rec.py -c configs/rec/rec_r31_sar.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
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```
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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SAR文本识别训练过程中保存的模型，转换成inference model。（ [模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_r31_sar_train.tar) )，可以使用如下命令进行转换：
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```
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python3 tools/export_model.py -c configs/rec/rec_r31_sar.yml -o Global.pretrained_model=./rec_r31_sar_train/best_accuracy Global.save_inference_dir=./inference/rec_sar
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```
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SAR文本识别模型推理，可以执行如下命令：
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```
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2022-08-16 09:34:48 +00:00
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python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./inference/rec_sar/" --rec_image_shape="3, 48, 48, 160" --rec_algorithm="SAR" --rec_char_dict_path="ppocr/utils/dict90.txt" --max_text_length=30 --use_space_char=False
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2022-04-28 02:28:14 +00:00
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```
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<a name="4-2"></a>
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### 4.2 C++推理
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由于C++预处理后处理还未支持SAR，所以暂未支持
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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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暂不支持
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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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暂不支持
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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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@article{Li2019ShowAA,
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title={Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition},
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author={Hui Li and Peng Wang and Chunhua Shen and Guyu Zhang},
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journal={ArXiv},
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year={2019},
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volume={abs/1811.00751}
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}
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```
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