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Add add_new_algorithm.md

Add add_new_algorithm.md
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- [Two-stage Algorithm](./doc/doc_en/algorithm_overview_en.md)
- [PGNet Algorithm](./doc/doc_en/algorithm_overview_en.md)
- [Python Inference](./doc/doc_en/inference_en.md)
- [Use PaddleOCR Architecture to Add New Algorithms](./doc/doc_en/add_new_algorithm_en.md)
- Data Annotation and Synthesis
- [Semi-automatic Annotation Tool: PPOCRLabel](./PPOCRLabel/README.md)
- [Data Synthesis Tool: Style-Text](./StyleText/README.md)
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- [Other Data Synthesis Tools](./doc/doc_en/data_synthesis_en.md)
- Datasets
- [General OCR Datasets(Chinese/English)](./doc/doc_en/datasets_en.md)
- [HandWritten_OCR_Datasets(Chinese)](./doc/doc_en/handwritten_datasets_en.md)
- [Handwritten OCR Datasets(Chinese)](./doc/doc_en/handwritten_datasets_en.md)
- [Various OCR Datasets(multilingual)](./doc/doc_en/vertical_and_multilingual_datasets_en.md)
- [Visualization](#Visualization)
- [New language requests](#language_requests)
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- PP-OCR产业落地:从训练到部署
- [PP-OCR模型库](./doc/doc_ch/models.md)
- [PP-OCR模型下载](./doc/doc_ch/models_list.md)
- [PP-OCR模型库Python推理](./doc/doc_ch/inference_ppocr.md)
- [基于Python引擎的PP-OCR模型库推理](./doc/doc_ch/inference_ppocr.md)
- [PP-OCR模型训练](./doc/doc_ch/training.md)
- [文本检测](./doc/doc_ch/detection.md)
- [文本识别](./doc/doc_ch/recognition.md)
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- [其它数据标注工具](./doc/doc_ch/data_annotation.md)
- [其它数据合成工具](./doc/doc_ch/data_synthesis.md)
- OCR学术圈
- [两阶段模型介绍与下载](./doc/doc_ch/algorithm_overview.md)
- [两阶段算法](./doc/doc_ch/algorithm_overview.md)
- [端到端PGNet算法](./doc/doc_ch/pgnet.md)
- [基于Python脚本预测引擎推理](./doc/doc_ch/inference.md)
- [使用PaddleOCR架构添加新算法](./doc/doc_ch/add_new_algorithm.md)
- 数据集
- [通用中英文OCR数据集](./doc/doc_ch/datasets.md)
- [手写中文OCR数据集](./doc/doc_ch/handwritten_datasets.md)
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# PP-OCR模型库Python推理
# 基于Python引擎的PP-OCR模型库推理
本文介绍针对PP-OCR模型库的Python推理引擎使用方法,内容依次为文本检测、文本识别、方向分类器以及三者串联在CPU、GPU上的预测方法。
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# Add new algorithm
# Add New Algorithm
PaddleOCR decomposes an algorithm into the following parts, and modularizes each part to make it more convenient to develop new algorithms.
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main_indicator: acc
```
## 优化器
## Optimizer
The optimizer is used to train the network. The optimizer also contains network regularization and learning rate decay modules. This part is under [ppocr/optimizer](../../ppocr/optimizer). PaddleOCR has built-in
Commonly used optimizer modules such as `Momentum`, `Adam` and `RMSProp`, common regularization modules such as `Linear`, `Cosine`, `Step` and `Piecewise`, and common learning rate decay modules such as `L1Decay` and `L2Decay`.
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# Environment Preparation
Windows and Mac users are recommended to use Anaconda to build a Python environment, and Linux users are recommended to use docker to build a Python environment. If you are familiar with the Python environment, you can skip to step 2 to install PaddlePaddle.
* [1. Python Environment Setup](#1)
+ [1.1 Windows](#1.1)
+ [1.2 Mac](#1.2)
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