2025.8.21 v3.2.0 released
-
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.
2025.8.21 v3.2.0 发布
-
重要模型新增：
- 新增 PP-OCRv5 英文、泰文、希腊文识别模型的训练、推理、部署。其中 PP-OCRv5 英文模型较 PP-OCRv5 主模型在英文场景提升 11%，泰文识别模型精度 82.68%，希腊文识别模型精度 89.28%。
-
部署能力升级：
- 全面支持飞桨框架 3.1.0 和 3.1.1 版本。
- 全面升级 PP-OCRv5 C++ 本地部署方案，支持 Linux、Windows，功能及精度效果与 Python 方案保持一致。
- 高性能推理支持 CUDA 12，可使用 Paddle Inference、ONNX Runtime 后端推理。
- 高稳定性服务化部署方案全面开源，支持用户根据需求对 Docker 镜像和 SDK 进行定制化修改。
- 高稳定性服务化部署方案支持通过手动构造HTTP请求的方式调用，该方式允许客户端代码使用任意编程语言编写。
-
Benchmark支持：
- 全部产线支持产线细粒度 benchmark，能够测量产线端到端推理时间以及逐层、逐模块的耗时数据，可用于辅助产线性能分析。
- 文档中补充各产线常用配置在主流硬件上的关键指标，包括推理耗时和内存占用等，为用户部署提供参考。
-
Bug修复：
- 修复模型训练时训练日志保存失败的问题。
- 对公式模型的数据增强部分进行了版本兼容性升级，以适应新版本的 albumentations 依赖，并修复了在多进程使用 tokenizers 依赖包时出现的死锁警告。
- 修复 PP-StructureV3 配置文件中的
use_chart_parsing等开关行为与其他产线不统一的问题。
-
其他升级：
- 分离必要依赖与可选依赖。使用基础文字识别功能时，仅需安装少量核心依赖；若需文档解析、信息抽取等功能，用户可按需选择安装额外依赖。
- 支持 Windows 用户使用英伟达 50 系显卡，可根据安装文档安装对应版本的 paddle 框架。
- PP-OCR 系列模型支持返回单文字坐标。
- 模型新增 AIStudio、ModelScope 等下载源。可指定相关下载源下载对应的模型。
- 支持图表转表 PP-Chart2Table 单功能模块推理能力。
- 优化部分使用文档中的描述，提升易用性。
New Contributors
Full Changelog: v3.1.1...v3.2.0