opendatalab/MinerU magic_pdf-1.3.8-released

on GitHub

What's Changed

• 1.3.8 Released

  • The default ocr model (ch) has been updated to PP-OCRv4_server_rec_doc (model update required)
    • PP-OCRv4_server_rec_doc is trained on a mix of more Chinese document data and PP-OCR training data, enhancing recognition capabilities for some traditional Chinese characters, Japanese, and special characters. It supports over 15,000 recognizable characters, improving text recognition in documents while also boosting general text recognition.
    • Performance comparison between PP-OCRv4_server_rec_doc, PP-OCRv4_server_rec, and PP-OCRv4_mobile_rec
    • Verified results show that the PP-OCRv4_server_rec_doc model significantly improves accuracy in both single-language (Chinese, English, Japanese, Traditional Chinese) and mixed-language scenarios, with speed comparable to PP-OCRv4_server_rec, making it suitable for most use cases.
    • In a small number of pure English scenarios, the PP-OCRv4_server_rec_doc model may encounter word concatenation issues, whereas PP-OCRv4_server_rec performs better in such cases. Therefore, we have retained the PP-OCRv4_server_rec model, which users can invoke by passing the parameter lang='ch_server'(python api) or --lang ch_server(cli).

• 1.3.8 发布

  • ocr默认模型(ch)更新为PP-OCRv4_server_rec_doc（需更新模型）
    • PP-OCRv4_server_rec_doc是在PP-OCRv4_server_rec的基础上，在更多中文文档数据和PP-OCR训练数据的混合数据训练而成，增加了部分繁体字、日文、特殊字符的识别能力，可支持识别的字符为1.5万+，除文档相关的文字识别能力提升外，也同时提升了通用文字的识别能力。
    • PP-OCRv4_server_rec_doc/PP-OCRv4_server_rec/PP-OCRv4_mobile_rec 性能对比
    • 经验证，PP-OCRv4_server_rec_doc模型在中英日繁单种语言或多种语言混合场景均有明显精度提升，且速度与PP-OCRv4_server_rec相当，适合绝大部分场景使用。
    • PP-OCRv4_server_rec_doc在小部分纯英文场景可能会发生单词粘连问题，PP-OCRv4_server_rec则在此场景下表现更好，因此我们保留了PP-OCRv4_server_rec模型，用户可通过增加参数lang='ch_server'(python api)或--lang ch_server(命令行)调用。

Full Changelog: magic_pdf-1.3.7-released...magic_pdf-1.3.8-released