Recently Update
Recently Update¶
🔥🔥 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_lenin the configuration has been changed from 736 to 64. - Added a new text line orientation classification model
PP-LCNet_x1_0_textline_oriwith 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_orientationparameter. - CPU inference speed optimisation: All pipeline CPU inferences now enable MKL-DNN by default.
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Support for C++ inference: The detection and recognition concatenation part of PP-OCRv5 now supports C++ inference.
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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 systemduring inference. - Fixed type hint issues in some Python 3.8 environments.
- Fixed the issue where the method
PPStructureV3.concatenate_markdown_pageswas missing. - Fixed an issue where specifying both
langandmodel_namewhen instantiatingpaddleocr.PaddleOCRresulted inmodel_namebeing 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.
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Achieves a 13% accuracy improvement over the previous generation.
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PP-StructureV3: General Document Parsing Solution
- Offers high-precision parsing for multi-scene, multi-layout PDFs.
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Outperforms numerous open and closed-source solutions in public benchmarks.
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PP-ChatOCRv4: Intelligent Document Understanding Solution
- Natively supports ERNIE 4.5 Turbo.
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Delivers a 15% accuracy boost over the previous version.
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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.
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Supports high-performance inference, serving, and edge deployment scenarios.
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Optimized Training with PaddlePaddle Framework 3.0:
- Compatible with the latest features such as the CINN compiler.
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Inference model files now use
xxx.jsoninstead ofxxx.pdmodel. -
Unified Model Naming:
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Updated naming conventions for models supported by PaddleOCR 3.0 for consistency and easier maintenance.
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For more details, check out the Upgrade Notes from 2.x to 3.x.
🔥🔥2025.3.7 release PaddleOCR v2.10, including:¶
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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. mAP@0.5 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.
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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:¶
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PaddleX, an All-in-One development tool based on PaddleOCR's advanced technology, supports low-code full-process development capabilities in the OCR field:
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🎨 Rich Model One-Click Call: Integrates 17 models related to text image intelligent analysis, general OCR, general layout parsing, table recognition, formula recognition, and seal recognition into 6 pipelines, which can be quickly experienced through a simple Python API one-click call. In addition, the same set of APIs also supports a total of 200+ models in image classification, object detection, image segmentation, and time series forecasting, forming 20+ single-function modules, making it convenient for developers to use model combinations.
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🚀 High Efficiency and Low barrier of entry: Provides two methods based on unified commands and GUI to achieve simple and efficient use, combination, and customization of models. Supports multiple deployment methods such as high-performance inference, service-oriented deployment, and edge deployment. Additionally, for various mainstream hardware such as NVIDIA GPU, Kunlunxin XPU, Ascend NPU, Cambricon MLU, and Haiguang DCU, models can be developed with seamless switching.
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Supports PP-ChatOCRv3-doc, high-precision layout detection model based on RT-DETR and high-efficiency layout area detection model based on PicoDet, high-precision table structure recognition model, text image unwarping model UVDoc, formula recognition model LatexOCR, and document image orientation classification model based on PP-LCNet.
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.