# PaddleOCR
**Repository Path**: xu_pengjun/PaddleOCR
## Basic Information
- **Project Name**: PaddleOCR
- **Description**: 基于飞桨的OCR和文档解析工具库,包含文字识别PP-OCR系列模型、文档解析PP-Structure系列方案和关键信息抽取PP-ChatOCR系列方案
- **Primary Language**: Python
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: https://paddlepaddle.github.io/PaddleOCR/latest/
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 1056
- **Created**: 2025-07-03
- **Last Updated**: 2025-07-03
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
[中文](./readme_c.md)| English
[](https://github.com/PaddlePaddle/PaddleOCR)
[](https://pypi.org/project/PaddleOCR/)



[](https://www.paddleocr.ai/)
[](https://aistudio.baidu.com/community/app/91660/webUI)
[](https://aistudio.baidu.com/community/app/518494/webUI)
[](https://aistudio.baidu.com/community/app/518493/webUI)
## 🚀 Introduction
Since its initial release, PaddleOCR has gained widespread acclaim across academia, industry, and research communities, thanks to its cutting-edge algorithms and proven performance in real-world applications. It’s already powering popular open-source projects like Umi-OCR, OmniParser, MinerU, and RAGFlow, making it the go-to OCR toolkit for developers worldwide.
On May 20, 2025, the PaddlePaddle team unveiled PaddleOCR 3.0, fully compatible with the official release of the **PaddlePaddle 3.0** framework. This update further **boosts text-recognition accuracy**, adds support for **multiple text-type recognition** and **handwriting recognition**, and meets the growing demand from large-model applications for **high-precision parsing of complex documents**. When combined with the **ERNIE 4.5T**, it significantly enhances key-information extraction accuracy. PaddleOCR 3.0 also introduces support for domestic hardware platforms such as **KUNLUNXIN** and **Ascend**. For the complete usage documentation, please refer to the [PaddleOCR 3.0 Documentation](https://paddlepaddle.github.io/PaddleOCR/latest/en/index.html).
Three Major New Features in PaddleOCR 3.0:
- Universal-Scene Text Recognition Model [PP-OCRv5](./docs/version3.x/algorithm/PP-OCRv5/PP-OCRv5.en.md): A single model that handles five different text types plus complex handwriting. Overall recognition accuracy has increased by 13 percentage points over the previous generation. [Online Demo](https://aistudio.baidu.com/community/app/91660/webUI)
- General Document-Parsing Solution [PP-StructureV3](./docs/version3.x/algorithm/PP-StructureV3/PP-StructureV3.en.md): Delivers high-precision parsing of multi-layout, multi-scene PDFs, outperforming many open- and closed-source solutions on public benchmarks. [Online Demo](https://aistudio.baidu.com/community/app/518494/webUI)
- Intelligent Document-Understanding Solution [PP-ChatOCRv4](./docs/version3.x/algorithm/PP-ChatOCRv4/PP-ChatOCRv4.en.md): Natively powered by the WenXin large model 4.5T, achieving 15 percentage points higher accuracy than its predecessor. [Online Demo](https://aistudio.baidu.com/community/app/518493/webUI)
In addition to providing an outstanding model library, PaddleOCR 3.0 also offers user-friendly tools covering model training, inference, and service deployment, so developers can rapidly bring AI applications to production.
## 📣 Recent updates
#### 🔥🔥**2025.06.19: Release of PaddleOCR 3.0.2, includes:**
- **New Features:**
- The default download source has been changed from `BOS` to `HuggingFace`. Users can also change the environment variable `PADDLE_PDX_MODEL_SOURCE` to `BOS` to set the model download source back to Baidu Object Storage (BOS).
- Added service invocation examples for six languages—C++, Java, Go, C#, Node.js, and PHP—for pipelines like PP-OCRv5, PP-StructureV3, and PP-ChatOCRv4.
- Improved the layout partition sorting algorithm in the PP-StructureV3 pipeline, enhancing the sorting logic for complex vertical layouts to deliver better results.
- Enhanced model selection logic: when a language is specified but a model version is not, the system will automatically select the latest model version supporting that language.
- Set a default upper limit for MKL-DNN cache size to prevent unlimited growth, while also allowing users to configure cache capacity.
- Updated default configurations for high-performance inference to support Paddle MKL-DNN acceleration and optimized the logic for automatic configuration selection for smarter choices.
- Adjusted the logic for obtaining the default device to consider the actual support for computing devices by the installed Paddle framework, making program behavior more intuitive.
- Added Android example for PP-OCRv5. [Details](https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/deployment/on_device_deployment.html).
- **Bug Fixes:**
- Fixed an issue with some CLI parameters in PP-StructureV3 not taking effect.
- Resolved an issue where `export_paddlex_config_to_yaml` would not function correctly in certain cases.
- Corrected the discrepancy between the actual behavior of `save_path` and its documentation description.
- Fixed potential multithreading errors when using MKL-DNN in basic service deployment.
- Corrected channel order errors in image preprocessing for the Latex-OCR model.
- Fixed channel order errors in saving visualized images within the text recognition module.
- Resolved channel order errors in visualized table results within PP-StructureV3 pipeline.
- Fixed an overflow issue in the calculation of `overlap_ratio` under extremely special circumstances in the PP-StructureV3 pipeline.
- **Documentation Improvements:**
- Updated the description of the `enable_mkldnn` parameter in the documentation to accurately reflect the program's actual behavior.
- Fixed errors in the documentation regarding the `lang` and `ocr_version` parameters.
- Added instructions for exporting production line configuration files via CLI.
- Fixed missing columns in the performance data table for PP-OCRv5.
- Refined benchmark metrics for PP-StructureV3 across different configurations.
- **Others:**
- Relaxed version restrictions on dependencies like numpy and pandas, restoring support for Python 3.12.
History Log
#### **2025.06.05: Release of PaddleOCR 3.0.1, includes:**
- **Optimisation of certain models and model configurations:**
- Updated the default model configuration for PP-OCRv5, changing both detection and recognition from mobile to server models. To improve default performance in most scenarios, the parameter `limit_side_len` in the configuration has been changed from 736 to 64.
- Added a new text line orientation classification model `PP-LCNet_x1_0_textline_ori` with an accuracy of 99.42%. The default text line orientation classifier for OCR, PP-StructureV3, and PP-ChatOCRv4 pipelines has been updated to this model.
- Optimised the text line orientation classification model `PP-LCNet_x0_25_textline_ori`, improving accuracy by 3.3 percentage points to a current accuracy of 98.85%.
- **Optimizations and fixes for some issues in version 3.0.0, [details](https://paddlepaddle.github.io/PaddleOCR/latest/en/update/update.html)**
🔥🔥2025.05.20: Official Release of **PaddleOCR v3.0**, including:
- **PP-OCRv5**: High-Accuracy Text Recognition Model for All Scenarios - Instant Text from Images/PDFs.
1. 🌐 Single-model support for **five** text types - Seamlessly process **Simplified Chinese, Traditional Chinese, Simplified Chinese Pinyin, English** and **Japanese** within a single model.
2. ✍️ Improved **handwriting recognition**: Significantly better at complex cursive scripts and non-standard handwriting.
3. 🎯 **13-point accuracy gain** over PP-OCRv4, achieving state-of-the-art performance across a variety of real-world scenarios.
- **PP-StructureV3**: General-Purpose Document Parsing – Unleash SOTA Images/PDFs Parsing for Real-World Scenarios!
1. 🧮 **High-Accuracy multi-scene PDF parsing**, leading both open- and closed-source solutions on the OmniDocBench benchmark.
2. 🧠 Specialized capabilities include **seal recognition**, **chart-to-table conversion**, **table recognition with nested formulas/images**, **vertical text document parsing**, and **complex table structure analysis**.
- **PP-ChatOCRv4**: Intelligent Document Understanding – Extract Key Information, not just text from Images/PDFs.
1. 🔥 **15-point accuracy gain** in key-information extraction on PDF/PNG/JPG files over the previous generation.
2. 💻 Native support for **ERINE4.5 Turbo**, with compatibility for large-model deployments via PaddleNLP, Ollama, vLLM, and more.
3. 🤝 Integrated [PP-DocBee2](https://github.com/PaddlePaddle/PaddleMIX/tree/develop/paddlemix/examples/ppdocbee2), enabling extraction and understanding of printed text, handwriting, seals, tables, charts, and other common elements in complex documents.
[History Log](https://paddlepaddle.github.io/PaddleOCR/latest/en/update/update.html)
## ⚡ Quick Start
### 1. Run online demo
[](https://aistudio.baidu.com/community/app/91660/webUI)
[](https://aistudio.baidu.com/community/app/518494/webUI)
[](https://aistudio.baidu.com/community/app/518493/webUI)
### 2. Installation
Install PaddlePaddle refer to [Installation Guide](https://www.paddlepaddle.org.cn/en/install/quick?docurl=/documentation/docs/en/develop/install/pip/linux-pip_en.html), after then, install the PaddleOCR toolkit.
```bash
# Install paddleocr
pip install paddleocr
```
### 3. Run inference by CLI
```bash
# Run PP-OCRv5 inference
paddleocr ocr -i https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png --use_doc_orientation_classify False --use_doc_unwarping False --use_textline_orientation False
# Run PP-StructureV3 inference
paddleocr pp_structurev3 -i https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/pp_structure_v3_demo.png --use_doc_orientation_classify False --use_doc_unwarping False
# Get the Qianfan API Key at first, and then run PP-ChatOCRv4 inference
paddleocr pp_chatocrv4_doc -i https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/vehicle_certificate-1.png -k 驾驶室准乘人数 --qianfan_api_key your_api_key --use_doc_orientation_classify False --use_doc_unwarping False
# Get more information about "paddleocr ocr"
paddleocr ocr --help
```
### 4. Run inference by API
**4.1 PP-OCRv5 Example**
```python
# Initialize PaddleOCR instance
ocr = PaddleOCR(
use_doc_orientation_classify=False,
use_doc_unwarping=False,
use_textline_orientation=False)
# Run OCR inference on a sample image
result = ocr.predict(
input="https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png")
# Visualize the results and save the JSON results
for res in result:
res.print()
res.save_to_img("output")
res.save_to_json("output")
```
4.2 PP-StructureV3 Example
```python
from pathlib import Path
from paddleocr import PPStructureV3
pipeline = PPStructureV3()
# For Image
output = pipeline.predict(
input="https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/pp_structure_v3_demo.png",
use_doc_orientation_classify=False,
use_doc_unwarping=False
)
# Visualize the results and save the JSON results
for res in output:
res.print()
res.save_to_json(save_path="output")
res.save_to_markdown(save_path="output")
```
4.3 PP-ChatOCRv4 Example
```python
from paddleocr import PPChatOCRv4Doc
chat_bot_config = {
"module_name": "chat_bot",
"model_name": "ernie-3.5-8k",
"base_url": "https://qianfan.baidubce.com/v2",
"api_type": "openai",
"api_key": "api_key", # your api_key
}
retriever_config = {
"module_name": "retriever",
"model_name": "embedding-v1",
"base_url": "https://qianfan.baidubce.com/v2",
"api_type": "qianfan",
"api_key": "api_key", # your api_key
}
pipeline = PPChatOCRv4Doc(
use_doc_orientation_classify=False,
use_doc_unwarping=False
)
visual_predict_res = pipeline.visual_predict(
input="https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/vehicle_certificate-1.png",
use_common_ocr=True,
use_seal_recognition=True,
use_table_recognition=True,
)
mllm_predict_info = None
use_mllm = False
# If a multimodal large model is used, the local mllm service needs to be started. You can refer to the documentation: https://github.com/PaddlePaddle/PaddleX/blob/release/3.0/docs/pipeline_usage/tutorials/vlm_pipelines/doc_understanding.m d performs deployment and updates the mllm_chat_bot_config configuration.
if use_mllm:
mllm_chat_bot_config = {
"module_name": "chat_bot",
"model_name": "PP-DocBee",
"base_url": "http://127.0.0.1:8080/", # your local mllm service url
"api_type": "openai",
"api_key": "api_key", # your api_key
}
mllm_predict_res = pipeline.mllm_pred(
input="https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/vehicle_certificate-1.png",
key_list=["驾驶室准乘人数"],
mllm_chat_bot_config=mllm_chat_bot_config,
)
mllm_predict_info = mllm_predict_res["mllm_res"]
visual_info_list = []
for res in visual_predict_res:
visual_info_list.append(res["visual_info"])
layout_parsing_result = res["layout_parsing_result"]
vector_info = pipeline.build_vector(
visual_info_list, flag_save_bytes_vector=True, retriever_config=retriever_config
)
chat_result = pipeline.chat(
key_list=["驾驶室准乘人数"],
visual_info=visual_info_list,
vector_info=vector_info,
mllm_predict_info=mllm_predict_info,
chat_bot_config=chat_bot_config,
retriever_config=retriever_config,
)
print(chat_result)
```
### 5. Domestic AI Accelerators
- [Huawei Ascend](https://paddlepaddle.github.io/PaddleOCR/latest/version3.x/other_devices_support/paddlepaddle_install_NPU.html)
- [KUNLUNXIN](https://paddlepaddle.github.io/PaddleOCR/latest/version3.x/other_devices_support/paddlepaddle_install_XPU.html)
## ⛰️ Advanced Tutorials
- [PP-OCRv5 Tutorial](https://paddlepaddle.github.io/PaddleOCR/latest/version3.x/pipeline_usage/OCR.html)
- [PP-StructureV3 Tutorial](https://paddlepaddle.github.io/PaddleOCR/latest/version3.x/pipeline_usage/PP-StructureV3.html)
- [PP-ChatOCRv4 Tutorial](https://paddlepaddle.github.io/PaddleOCR/latest/version3.x/pipeline_usage/PP-ChatOCRv4.html)
## 🔄 Quick Overview of Execution Results
## 👩👩👧👦 Community
| PaddlePaddle WeChat official account | Join the tech discussion group |
| :---: | :---: |
|
|
|
## 😃 Awesome Projects Leveraging PaddleOCR
PaddleOCR wouldn’t be where it is today without its incredible community! 💗 A massive thank you to all our longtime partners, new collaborators, and everyone who’s poured their passion into PaddleOCR — whether we’ve named you or not. Your support fuels our fire!
| Project Name | Description |
| ------------ | ----------- |
| [RAGFlow](https://github.com/infiniflow/ragflow)
|RAG engine based on deep document understanding.|
| [MinerU](https://github.com/opendatalab/MinerU)
|Multi-type Document to Markdown Conversion Tool|
| [Umi-OCR](https://github.com/hiroi-sora/Umi-OCR)
|Free, Open-source, Batch Offline OCR Software.|
| [OmniParser](https://github.com/microsoft/OmniParser)
|OmniParser: Screen Parsing tool for Pure Vision Based GUI Agent.|
| [QAnything](https://github.com/netease-youdao/QAnything)
|Question and Answer based on Anything.|
| [PDF-Extract-Kit](https://github.com/opendatalab/PDF-Extract-Kit)
|A powerful open-source toolkit designed to efficiently extract high-quality content from complex and diverse PDF documents.|
| [Dango-Translator](https://github.com/PantsuDango/Dango-Translator)
|Recognize text on the screen, translate it and show the translation results in real time.|
| [Learn more projects](./awesome_projects.md) | [More projects based on PaddleOCR](./awesome_projects.md)|
## 👩👩👧👦 Contributors
## 🌟 Star
[](https://star-history.com/#PaddlePaddle/PaddleOCR&Date)
## 📄 License
This project is released under the [Apache 2.0 license](LICENSE).
## 🎓 Citation
```
@misc{paddleocr2020,
title={PaddleOCR, Awesome multilingual OCR toolkits based on PaddlePaddle.},
author={PaddlePaddle Authors},
howpublished = {\url{https://github.com/PaddlePaddle/PaddleOCR}},
year={2020}
}
```