Hybrid Language Enhancement Program
Key techniques for improving cross-language document processing accuracy:
- Language Statement::
- Specify the main language explicitly at the beginning of the prompt: 'DOC_LANG=Chinese-based, with English terminology'
- Wrap foreign language content in {{en}}...{{/en}} tags for specific passages
- preprocessing skills::
- Use OpenCV's MSER algorithm to first separate different language text regions
- Use the -layout-analysis parameter for bilingual cross-referenced documents to keep paragraphs aligned.
- model parameter::
- Add -lang=zh-en-fr to support multi-language mixed decoding
- set-tolerant=0.2 Allow 20% non-dominant language character differences
- Post-processing validation::
- Checking the output language distribution with the LangDetect library
- Calling Google/Baidu thesaurus proofreading for specialized terminology
Comparison of results: 821 TP3T of Chinese-English mixing accuracy without optimization → up to 941 TP3T with the above scheme.
This answer comes from the articleQwen2.5-VL: an open source multimodal grand model supporting image-video document parsingThe
May not be reproduced without permission:AI productivity tools " How to overcome the problem of decreasing text recognition accuracy in mixed multilingual documents?
