github.com/PaddlePaddle/PaddleOCR

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Introduction

PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools that help users train better models and apply them into practice.

Recent updates

• 🔥2022.8.24 Release PaddleOCR release/2.6

  • Release PP-Structurev2，with functions and performance fully upgraded, adapted to Chinese scenes, and new support for Layout Recovery and one line command to convert PDF to Word;
  • Layout Analysis optimization: model storage reduced by 95%, while speed increased by 11 times, and the average CPU time-cost is only 41ms;
  • Table Recognition optimization: 3 optimization strategies are designed, and the model accuracy is improved by 6% under comparable time consumption;
  • Key Information Extraction optimization：a visual-independent model structure is designed, the accuracy of semantic entity recognition is increased by 2.8%, and the accuracy of relation extraction is increased by 9.1%.

• 🔥2022.7 Release OCR scene application collection

  • Release 9 vertical models such as digital tube, LCD screen, license plate, handwriting recognition model, high-precision SVTR model, etc, covering the main OCR vertical applications in general, manufacturing, finance, and transportation industries.

• 🔥2022.5.9 Release PaddleOCR release/2.5

  • Release PP-OCRv3: With comparable speed, the effect of Chinese scene is further improved by 5% compared with PP-OCRv2, the effect of English scene is improved by 11%, and the average recognition accuracy of 80 language multilingual models is improved by more than 5%.
  • Release PPOCRLabelv2: Add the annotation function for table recognition task, key information extraction task and irregular text image.
  • Release interactive e-book "Dive into OCR", covers the cutting-edge theory and code practice of OCR full stack technology.

• more

Features

PaddleOCR support a variety of cutting-edge algorithms related to OCR, and developed industrial featured models/solution PP-OCR and PP-Structure on this basis, and get through the whole process of data production, model training, compression, inference and deployment.

It is recommended to start with the “quick experience” in the document tutorial

Quick Experience

• Web online experience for the ultra-lightweight OCR: Online Experience
• Mobile DEMO experience (based on EasyEdge and Paddle-Lite, supports iOS and Android systems): Sign in to the website to obtain the QR code for installing the App
• One line of code quick use: Quick Start

E-book: Dive Into OCR

• Dive Into OCR 📚

Community

• Join us👬: Scan the QR code below with your Wechat, you can join the official technical discussion group. Looking forward to your participation.

PP-OCR Series Model List（Update on September 8th）

Model introduction	Model name	Recommended scene	Detection model	Direction classifier	Recognition model
Chinese and English ultra-lightweight PP-OCRv3 model（16.2M）	ch_PP-OCRv3_xx	Mobile & Server	inference model / trained model	inference model / trained model	inference model / trained model
English ultra-lightweight PP-OCRv3 model（13.4M）	en_PP-OCRv3_xx	Mobile & Server	inference model / trained model	inference model / trained model	inference model / trained model
Chinese and English ultra-lightweight PP-OCRv2 model（11.6M）	ch_PP-OCRv2_xx	Mobile & Server	inference model / trained model	inference model / trained model	inference model / trained model
Chinese and English ultra-lightweight PP-OCR model (9.4M)	ch_ppocr_mobile_v2.0_xx	Mobile & server	inference model / trained model	inference model / trained model	inference model / trained model
Chinese and English general PP-OCR model (143.4M)	ch_ppocr_server_v2.0_xx	Server	inference model / trained model	inference model / trained model	inference model / trained model

• For more model downloads (including multiple languages), please refer to PP-OCR series model downloads.
• For a new language request, please refer to Guideline for new language_requests.
• For structural document analysis models, please refer to PP-Structure models.

Tutorials

• Environment Preparation
• PP-OCR 🔥
  • Quick Start
  • Model Zoo
  • Model training
    • Text Detection
    • Text Recognition
    • Text Direction Classification
  • Model Compression
    • Model Quantization
    • Model Pruning
    • Knowledge Distillation
  • Inference and Deployment
    • Python Inference
    • C++ Inference
    • Serving
    • Mobile
    • Paddle2ONNX
    • PaddleCloud
    • Benchmark
• PP-Structure 🔥
  • Quick Start
  • Model Zoo
  • Model training
    • Layout Analysis
    • Table Recognition
    • Key Information Extraction
  • Inference and Deployment
    • Python Inference
    • C++ Inference
    • Serving
• Academic Algorithms
  • Text detection
  • Text recognition
  • End-to-end OCR
  • Table Recognition
  • Key Information Extraction
  • Add New Algorithms to PaddleOCR
• Data Annotation and Synthesis
  • Semi-automatic Annotation Tool: PPOCRLabel
  • Data Synthesis Tool: Style-Text
  • Other Data Annotation Tools
  • Other Data Synthesis Tools
• Datasets
  • General OCR Datasets(Chinese/English)
  • HandWritten_OCR_Datasets(Chinese)
  • Various OCR Datasets(multilingual)
  • Layout Analysis
  • Table Recognition
  • Key Information Extraction
• Code Structure
• Visualization
• Community
• New language requests
• FAQ
• References
• License

Visualization more

PP-OCRv3 Chinese model

PP-OCRv3 English model

PP-OCRv3 Multilingual model

PP-Structurev2

• layout analysis + table recognition

• SER (Semantic entity recognition)

• RE (Relation Extraction)

Guideline for New Language Requests

If you want to request a new language support, a PR with 1 following files are needed：

1. In folder ppocr/utils/dict, it is necessary to submit the dict text to this path and name it with {language}_dict.txt that contains a list of all characters. Please see the format example from other files in that folder.

If your language has unique elements, please tell me in advance within any way, such as useful links, wikipedia and so on.

More details, please refer to Multilingual OCR Development Plan.

License

This project is released under Apache 2.0 license