github PaddlePaddle/PaddleOCR v3.6.0

5 hours ago

2026.5.28 v3.6.0 released

  • Release the PaddleOCR-VL-1.6 document parsing solution.

    • PaddleOCR-VL-1.6 is an upgraded compact document parsing model built upon PaddleOCR-VL-1.5. Through a newly introduced region-aware data optimization framework and a progressive post-training recipe, the model further enhances parsing robustness and overall recognition accuracy.
    • PaddleOCR-VL-1.6 achieves a new state-of-the-art score of 96.33% on the authoritative benchmark OmniDocBench v1.6, while also setting new records on OmniDocBench v1.5 and Real5-OmniDocBench. The model delivers leading performance across text, formula, and table recognition tasks, surpassing both open-source and closed-source solutions.
    • PaddleOCR-VL-1.6 significantly improves capabilities in table recognition, Chinese ancient document parsing, and Chinese rare character recognition, while further enhancing performance in seal recognition, text spotting, chart recognition, and other diverse document scenarios.
    • The model architecture remains fully compatible with PaddleOCR-VL-1.5, enabling zero-cost plug-and-play migration for existing users.
  • Release official PaddleOCR API SDKs for Python, Go, and TypeScript, enabling more convenient integration with PaddleOCR official asynchronous APIs.

  • Add support for parsing multi-page TIFF files.

2026.5.28 v3.6.0 发布

  • 发布 PaddleOCR-VL-1.6 文档解析方案。

    • PaddleOCR-VL-1.6 是基于 PaddleOCR-VL-1.5 全新升级的轻量级文档解析模型。通过引入全新的区域感知数据优化框架以及渐进式后训练优化方案,模型进一步提升了复杂文档场景下的解析鲁棒性与整体识别精度。
    • PaddleOCR-VL-1.6 在权威文档解析评测集 OmniDocBench v1.6 上取得了 96.33% 的全新 SOTA 成绩,并同时刷新了 OmniDocBench v1.5 与 Real5-OmniDocBench 的最佳纪录。在文本、公式、表格等核心任务上,模型均展现出全面领先的性能,超越当前主流开源及闭源方案。
    • PaddleOCR-VL-1.6 在表格识别中文古籍文档解析中文生僻字识别等能力上实现显著增强,同时进一步提升了印章识别文本检测识别(Spotting)图表解析等多种复杂场景下的表现。
    • PaddleOCR-VL-1.6 与 PaddleOCR-VL-1.5 保持完全兼容,无需额外适配即可实现零成本即插即用升级
  • 发布 PaddleOCR 官方 API Python、Go、TypeScript SDK,支持更加便捷地调用 PaddleOCR 官方异步 API。

  • 新增对多页 TIFF 文件解析的支持。

Full Changelog: v3.5.0...v3.6.0

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