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81 lines
2.1 KiB
Markdown
81 lines
2.1 KiB
Markdown
# SEED
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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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> [STAR-Net: a spatial attention residue network for scene text recognition.](https://arxiv.org/pdf/2005.10977.pdf)
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> Qiao, Zhi and Zhou, Yu and Yang, Dongbao and Zhou, Yucan and Wang, Weiping
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> CVPR, 2020
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参考[DTRB](https://arxiv.org/abs/1904.01906) 文字识别训练和评估流程，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，算法效果如下：
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|模型|骨干网络|Avg Accuracy|模型存储命名|下载链接|
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|---|---|---|---|---|
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|SEED|Aster_Resnet| 85.2% | rec_resnet_stn_bilstm_att | [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.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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<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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comming soon
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<a name="4-2"></a>
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### 4.2 C++推理
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comming soon
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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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comming soon
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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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comming soon
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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{qiao2020seed,
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title={Seed: Semantics enhanced encoder-decoder framework for scene text recognition},
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author={Qiao, Zhi and Zhou, Yu and Yang, Dongbao and Zhou, Yucan and Wang, Weiping},
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booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
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pages={13528--13537},
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year={2020}
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}
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```
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