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feat(docs): update changelog for PP-OCRv5 model support and handwritten document recognition enhancements
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README.md
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README.md
@@ -48,6 +48,20 @@ Easier to use: Just grab MinerU Desktop. No coding, no login, just a simple inte
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# Changelog
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- 2025/05/24 1.3.12 Released
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- Added support for ppocrv5 model, updated `ch_server` model to `PP-OCRv5_rec_server` and `ch_lite` model to `PP-OCRv5_rec_mobile` (model update required)
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- In testing, we found that ppocrv5(server) shows some improvement for handwritten documents, but slightly lower accuracy than v4_server_doc for other document types. Therefore, the default ch model remains unchanged as `PP-OCRv4_server_rec_doc`.
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- Since ppocrv5 enhances recognition capabilities for handwritten text and special characters, you can manually select ppocrv5 models for Japanese, traditional Chinese mixed scenarios and handwritten document scenarios
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- You can select the appropriate model through the lang parameter `lang='ch_server'` (python api) or `--lang ch_server` (command line):
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- `ch`: `PP-OCRv4_rec_server_doc` (default) (Chinese, English, Japanese, Traditional Chinese mixed/15k dictionary)
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- `ch_server`: `PP-OCRv5_rec_server` (Chinese, English, Japanese, Traditional Chinese mixed + handwriting/18k dictionary)
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- `ch_lite`: `PP-OCRv5_rec_mobile` (Chinese, English, Japanese, Traditional Chinese mixed + handwriting/18k dictionary)
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- `ch_server_v4`: `PP-OCRv4_rec_server` (Chinese, English mixed/6k dictionary)
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- `ch_lite_v4`: `PP-OCRv4_rec_mobile` (Chinese, English mixed/6k dictionary)
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- Added support for handwritten documents by optimizing layout recognition of handwritten text areas
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- This feature is supported by default, no additional configuration needed
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- You can refer to the instructions above to manually select ppocrv5 model for better handwritten document parsing
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- The demos on `huggingface` and `modelscope` have been updated to support handwriting recognition and ppocrv5 models, which you can experience online
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- 2025/04/29 1.3.10 Released
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- Support for custom formula delimiters can be achieved by modifying the `latex-delimiter-config` item in the `magic-pdf.json` file under the user directory.
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- 2025/04/27 1.3.9 Released
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@@ -47,6 +47,20 @@
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</div>
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# 更新记录
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- 2025/05/24 1.3.12 发布
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- 增加ppocrv5模型的支持,将`ch_server`模型更新为`PP-OCRv5_rec_server`,`ch_lite`模型更新为`PP-OCRv5_rec_mobile`(需更新模型)
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- 在测试中,发现ppocrv5(server)对手写文档效果有一定提升,但在其余类别文档的精度略差于v4_server_doc,因此默认的ch模型保持不变,仍为`PP-OCRv4_server_rec_doc`。
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- 由于ppocrv5强化了手写场景和特殊字符的识别能力,因此您可以在日繁混合场景以及手写文档场景下手动选择使用ppocrv5模型
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- 您可通过lang参数`lang='ch_server'`(python api)或`--lang ch_server`(命令行)自行选择相应的模型:
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- `ch` :`PP-OCRv4_rec_server_doc`(默认)(中英日繁混合/1.5w字典)
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- `ch_server` :`PP-OCRv5_rec_server`(中英日繁混合+手写场景/1.8w字典)
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- `ch_lite` :`PP-OCRv5_rec_mobile`(中英日繁混合+手写场景/1.8w字典)
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- `ch_server_v4` :`PP-OCRv4_rec_server`(中英混合/6k字典)
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- `ch_lite_v4` :`PP-OCRv4_rec_mobile`(中英混合/6k字典)
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- 增加手写文档的支持,通过优化layout对手写文本区域的识别,现已支持手写文档的解析
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- 默认支持此功能,无需额外配置
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- 可以参考上述说明,手动选择ppocrv5模型以获得更好的手写文档解析效果
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- `huggingface`和`modelscope`的demo已更新为支持手写识别和ppocrv5模型的版本,可自行在线体验
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- 2025/04/29 1.3.10 发布
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- 支持使用自定义公式标识符,可通过修改用户目录下的`magic-pdf.json`文件中的`latex-delimiter-config`项实现。
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- 2025/04/27 1.3.9 发布
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