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🔥🔥2025.08.21: Release of PaddleOCR 3.2.0, includes

  • Significant Model Additions:

    • Introduced training, inference, and deployment for PP-OCRv5 recognition models in English, Thai, and Greek. The PP-OCRv5 English model delivers an 11% improvement in English scenarios compared to the main PP-OCRv5 model, with the Thai and Greek recognition models achieving accuracies of 82.68% and 89.28%, respectively.

  • Deployment Capability Upgrades:

    • Full support for PaddlePaddle framework versions 3.1.0 and 3.1.1.
    • Comprehensive upgrade of the PP-OCRv5 C++ local deployment solution, now supporting both Linux and Windows, with feature parity and identical accuracy to the Python implementation.
    • High-performance inference now supports CUDA 12, and inference can be performed using either the Paddle Inference or ONNX Runtime backends.
    • The high-stability service-oriented deployment solution is now fully open-sourced, allowing users to customize Docker images and SDKs as required.
    • The high-stability service-oriented deployment solution also supports invocation via manually constructed HTTP requests, enabling client-side code development in any programming language.

  • Benchmark Support:

    • All production lines now support fine-grained benchmarking, enabling measurement of end-to-end inference time as well as per-layer and per-module latency data to assist with performance analysis.
    • Documentation has been updated to include key metrics for commonly used configurations on mainstream hardware, such as inference latency and memory usage, providing deployment references for users.

  • Bug Fixes:

    • Resolved the issue of failed log saving during model training.
    • Upgraded the data augmentation component for formula models for compatibility with newer versions of the albumentations dependency, and fixed deadlock warnings when using the tokenizers package in multi-process scenarios.
    • Fixed inconsistencies in switch behaviors (e.g., use_chart_parsing) in the PP-StructureV3 configuration files compared to other pipelines.

  • Other Enhancements:

    • Separated core and optional dependencies. Only minimal core dependencies are required for basic text recognition; additional dependencies for document parsing and information extraction can be installed as needed.
    • Enabled support for NVIDIA RTX 50 series graphics cards on Windows; users can refer to the installation guide for the corresponding PaddlePaddle framework versions.
    • PP-OCR series models now support returning single-character coordinates.
    • Added AIStudio, ModelScope, and other model download sources, allowing users to specify the source for model downloads.
    • Added support for chart-to-table conversion via the PP-Chart2Table module.
    • Optimized documentation descriptions to improve usability.

2025.08.15: Release of PaddleOCR 3.1.1, includes

  • Bug Fixes:

    • Added the missing methods save_vector, save_visual_info_list, load_vector, and load_visual_info_list in the PP-ChatOCRv4 class.
    • Added the missing parameters glossary and llm_request_interval to the translate method in the PPDocTranslation class.

  • Documentation Improvements:

    • Added a demo to the MCP documentation.
    • Added information about the PaddlePaddle and PaddleOCR version used for performance metrics testing in the documentation.
    • Fixed errors and omissions in the production line document translation.

  • Others:

    • Changed the MCP server dependency to use the pure Python library puremagic instead of python-magic to reduce installation issues.
    • Retested PP-OCRv5 performance metrics with PaddleOCR version 3.1.0 and updated the documentation.

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, 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 v3.0.2, which 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 pipeline across different configurations.

  • Others:

    • Relaxed version restrictions on dependencies like numpy and pandas, restoring support for Python 3.12.

2025.06.05: Release of PaddleOCR v3.0.1, which 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%.

  • Optimisation of issues present in version 3.0.0:

    • Improved CLI usage experience: When using the PaddleOCR CLI without passing any parameters, a usage prompt is now provided.
    • New parameters added: PP-ChatOCRv3 and PP-StructureV3 now support the use_textline_orientation parameter.
    • CPU inference speed optimisation: All pipeline CPU inferences now enable MKL-DNN by default.
    • Support for C++ inference: The detection and recognition concatenation part of PP-OCRv5 now supports C++ inference.

  • Fixes for issues present in version 3.0.0:

    • Fixed an issue where PP-StructureV3 encountered CPU inference errors due to the inability to use MKL-DNN with formula and table recognition models.
    • Fixed an issue where GPU environments encountered the error FatalError: Process abort signal is detected by the operating system during inference.
    • Fixed type hint issues in some Python 3.8 environments.
    • Fixed the issue where the method PPStructureV3.concatenate_markdown_pages was missing.
    • Fixed an issue where specifying both lang and model_name when instantiating paddleocr.PaddleOCR resulted in model_name being ineffective.

2025.05.20: PaddleOCR 3.0 Official Release Highlights

  • PP-OCRv5: All-Scene Text Recognition Model

    • Supports five text types and complex handwriting in a single model.
    • Achieves a 13% accuracy improvement over the previous generation.

  • PP-StructureV3: General Document Parsing Solution

    • Offers high-precision parsing for multi-scene, multi-layout PDFs.
    • Outperforms numerous open and closed-source solutions in public benchmarks.

  • PP-ChatOCRv4: Intelligent Document Understanding Solution

    • Natively supports ERNIE 4.5.
    • Delivers a 15% accuracy boost over the previous version.

  • Rebuilt Deployment Capabilities with Unified Inference Interface:

    • Integrates PaddleX3.0's core features for a comprehensive upgrade of the inference and deployment modules.
    • Optimizes the design from version 2.x and unifies the Python API and CLI.
    • Supports high-performance inference, serving, and on-device deployment scenarios.

  • Optimized Training with PaddlePaddle Framework 3.0:

    • Compatible with the latest features such as the CINN compiler.
    • Inference model files now use xxx.json instead of xxx.pdmodel.

  • Unified Model Naming:

    • Updated naming conventions for models supported by PaddleOCR 3.0 for consistency and easier maintenance.

  • For more details, check out the Upgrade Notes from 2.x to 3.x.

2025.3.7 release PaddleOCR v2.10, including

  • 12 new self-developed single models:

    • Layout Detection series with 3 models: PP-DocLayout-L, PP-DocLayout-M, PP-DocLayout-S, supporting prediction of 23 common layout categories. High-quality layout detection for various document types such as papers, reports, exams, books, magazines, contracts, newspapers in both English and Chinese. [email protected] reaches up to 90.4%, lightweight models can process over 100 pages of document images per second end-to-end.
    • Formula Recognition series with 2 models: PP-FormulaNet-L, PP-FormulaNet-S, supporting 50,000 common LaTeX vocabulary, capable of recognizing complex printed and handwritten formulas. PP-FormulaNet-L has 6 percentage points higher accuracy than models of the same level, and PP-FormulaNet-S is 16 times faster than models with similar accuracy.
    • Table Structure Recognition series with 2 models: SLANeXt_wired, SLANeXt_wireless. A newly developed table structure recognition model, supporting structured prediction for both wired and wireless tables. Compared to SLANet_plus, SLANeXt shows significant improvement in table structure, with 6 percentage points higher accuracy on internal high-difficulty table recognition evaluation sets.
    • Table Classification series with 1 model: PP-LCNet_x1_0_table_cls, an ultra-lightweight classification model for both wired and wireless tables.
    • Table Cell Detection series with 2 models: RT-DETR-L_wired_table_cell_det, RT-DETR-L_wireless_table_cell_det, supporting cell detection in both wired and wireless tables. These can be combined with SLANeXt_wired, SLANeXt_wireless, text detection, and text recognition modules for end-to-end table prediction. (See the newly added Table Recognition v2 pipeline)
    • Text Recognition series with 1 model: PP-OCRv4_server_rec_doc, supports over 15,000 characters, with a broader text recognition range, additionally improving the recognition accuracy of certain texts. The accuracy is more than 3 percentage points higher than PP-OCRv4_server_rec on internal datasets.
    • Text Line Orientation Classification series with 1 model: PP-LCNet_x0_25_textline_ori, an ultra-lightweight text line orientation classification model with only 0.3M storage.

  • 4 high-value multi-model combination solutions:

    • Document Image Preprocessing Pipeline: Achieve correction of distortion and orientation in document images through the combination of ultra-lightweight models.
    • Layout Parsing v2 Pipeline: Combines multiple self-developed different types of OCR models to optimize complex layout reading order, achieving end-to-end conversion of various complex PDF files to Markdown and JSON files. The conversion effect is better than other open-source solutions in multiple document scenarios. It can provide high-quality data production capabilities for large model training and application.
    • Table Recognition v2 Pipeline: Provides better table recognition capabilities. By combining table classification module, table cell detection module, table structure recognition module, text detection module, text recognition module, etc., it achieves prediction of various styles of tables. Users can customize and finetune any module to improve the effect of vertical tables.
    • PP-ChatOCRv4-doc Pipeline: Based on PP-ChatOCRv3-doc, integrating multi-modal large models, optimizing Prompt and multi-model combination post-processing logic. It effectively addresses common complex document information extraction challenges such as layout analysis, rare characters, multi-page PDFs, tables, and seal recognition, achieving 15 percentage points higher accuracy than PP-ChatOCRv3-doc. The large model upgrades local deployment capabilities, providing a standard OpenAI interface, supporting calls to locally deployed large models like DeepSeek-R1.

2024.10.18 release PaddleOCR v2.9, including

2022.5.9 release PaddleOCR v2.5, including

  • 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%.
  • PPOCRLabelv2: Add the annotation function for table recognition task, key information extraction task and irregular text image.
  • Interactive e-book "Dive into OCR", covers the cutting-edge theory and code practice of OCR full stack technology.

2022.5.7 Add support for metric and model logging during training to Weights & Biases

2021.12.21 OCR open source online course starts. The lesson starts at 8:30 every night and lasts for ten days. Free registration: https://aistudio.baidu.com/aistudio/course/introduce/25207

2021.12.21 release PaddleOCR v2.4, release 1 text detection algorithm (PSENet), 3 text recognition algorithms (NRTR、SEED、SAR), 1 key information extraction algorithm (SDMGR) and 3 DocVQA algorithms (LayoutLM、LayoutLMv2，LayoutXLM)

2021.9.7 release PaddleOCR v2.3, PP-OCRv2 is proposed. The CPU inference speed of PP-OCRv2 is 220% higher than that of PP-OCR server. The F-score of PP-OCRv2 is 7% higher than that of PP-OCR mobile

2021.8.3 released PaddleOCR v2.2, add a new structured documents analysis toolkit, i.e., PP-Structure, support layout analysis and table recognition (One-key to export chart images to Excel files)

2021.4.8 release end-to-end text recognition algorithm PGNet which is published in AAAI 2021. Find tutorial here；release multi language recognition models, support more than 80 languages recognition; especially, the performance of English recognition model is Optimized

2021.1.21 update more than 25+ multilingual recognition models models list, including：English, Chinese, German, French, Japanese，Spanish，Portuguese Russia Arabic and so on. Models for more languages will continue to be updated Develop Plan

2020.12.15 update Data synthesis tool, i.e., Style-Text，easy to synthesize a large number of images which are similar to the target scene image

2020.11.25 Update a new data annotation tool, i.e., PPOCRLabel, which is helpful to improve the labeling efficiency. Moreover, the labeling results can be used in training of the PP-OCR system directly

2020.9.22 Update the PP-OCR technical article, https://arxiv.org/abs/2009.09941

2020.9.19 Update the ultra lightweight compressed ppocr_mobile_slim series models, the overall model size is 3.5M, suitable for mobile deployment

2020.9.17 update English recognition model and Multilingual recognition model, English, Chinese, German, French, Japanese and Korean have been supported. Models for more languages will continue to be updated

2020.8.24 Support the use of PaddleOCR through whl package installation，please refer PaddleOCR Package

2020.8.16 Release text detection algorithm SAST and text recognition algorithm SRN

2020.7.23, Release the playback and PPT of live class on BiliBili station, PaddleOCR Introduction, address

2020.7.15, Add mobile App demo , support both iOS and Android (based on easyedge and Paddle Lite)

2020.7.15, Improve the deployment ability, add the C + + inference , serving deployment. In addition, the benchmarks of the ultra-lightweight Chinese OCR model are provided

2020.7.15, Add several related datasets, data annotation and synthesis tools

2020.7.9 Add a new model to support recognize the character "space"

2020.7.9 Add the data argument and learning rate decay strategies during training

2020.6.8 Add datasets and keep updating

2020.6.5 Support exporting attention model to inference_model

2020.6.5 Support separate prediction and recognition, output result score

2020.5.30 Provide Lightweight Chinese OCR online experience

2020.5.30 Model prediction and training support on Windows system

2020.5.30 Open source general Chinese OCR model

2020.5.14 Release PaddleOCR Open Class

2020.5.14 Release PaddleOCR Practice Notebook

2020.5.14 Open source 8.6M lightweight Chinese OCR model