PaddleOCR

🚀 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 4.5 Turbo, it significantly enhances key-information extraction accuracy. PaddleOCR 3.0 also introduces support for Chinese Heterogeneous AI Accelerators such as KUNLUNXIN and Ascend. For the complete usage documentation, please refer to the PaddleOCR 3.0 Documentation.

Three Major New Features in PaddleOCR 3.0:

• Universal-Scene Text Recognition Model PP-OCRv5: 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

• General Document-Parsing Solution PP-StructureV3: Delivers high-precision parsing of multi-layout, multi-scene PDFs, outperforming many open- and closed-source solutions on public benchmarks. Online Demo

• Intelligent Document-Understanding Solution PP-ChatOCRv4: Natively powered by the ERNIE 4.5 Turbo, achieving 15 percentage points higher accuracy than its predecessor. Online Demo

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

2025.06.29: Release of PaddleOCR 3.1.0, includes:

• Key Models and Pipelines:

  • Added PP-OCRv5 Multilingual Text Recognition Model, which supports the training and inference process for text recognition models in 37 languages, including French, Spanish, Portuguese, Russian, Korean, etc. Average accuracy improved by over 30%. Details
  • Upgraded the PP-Chart2Table model in PP-StructureV3, further enhancing the capability of converting charts to tables. On internal custom evaluation sets, the metric (RMS-F1) increased by 9.36 percentage points (71.24% -> 80.60%).
  • Newly launched document translation pipeline, PP-DocTranslation, based on PP-StructureV3 and ERNIE 4.5 Turbo, which supports the translation of Markdown format documents, various complex-layout PDF documents, and document images, with the results saved as Markdown format documents. Details

• New MCP server: Details

  • Supports both OCR and PP-StructureV3 pipelines.
  • Supports three working modes: local Python library, AIStudio Community Cloud Service, and self-hosted service.
  • Supports invoking local services via stdio and remote services via Streamable HTTP.

• Documentation Optimization: Improved the descriptions in some user guides for a smoother reading experience.

2025.06.26: Release of PaddleOCR 3.0.3, includes:

• Bug Fix: Resolved the issue where the enable_mkldnn parameter was not effective, restoring the default behavior of using MKL-DNN for CPU inference.

🔥🔥 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.

• 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.

⚡ Quick Start

1. Run online demo

2. Installation

Install PaddlePaddle refer to Installation Guide, after then, install the PaddleOCR toolkit.

# Install paddleocr
pip install paddleocr

3. Run inference by CLI

# 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

# Initialize PaddleOCR instance
from paddleocr import PaddleOCR
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")

from pathlib import Path
from paddleocr import PPStructureV3

pipeline = PPStructureV3(
    use_doc_orientation_classify=False,
    use_doc_unwarping=False
)

# For Image
output = pipeline.predict(
    input="https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/pp_structure_v3_demo.png",
)

# 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")           

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.en.md 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. Chinese Heterogeneous AI Accelerators

• Huawei Ascend
• KUNLUNXIN

⛰️ Advanced Tutorials

• PP-OCRv5 Tutorial
• PP-StructureV3 Tutorial
• PP-ChatOCRv4 Tutorial

🔄 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	RAG engine based on deep document understanding.
MinerU	Multi-type Document to Markdown Conversion Tool
Umi-OCR	Free, Open-source, Batch Offline OCR Software.
OmniParser	OmniParser: Screen Parsing tool for Pure Vision Based GUI Agent.
QAnything	Question and Answer based on Anything.
PDF-Extract-Kit	A powerful open-source toolkit designed to efficiently extract high-quality content from complex and diverse PDF documents.
Dango-Translator	Recognize text on the screen, translate it and show the translation results in real time.
Learn more projects	More projects based on PaddleOCR

👩‍👩‍👧‍👦 Contributors

🌟 Star

📄 License

This project is released under the Apache 2.0 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}
}