When unsatisfactory recognition accuracy occurs, the following steps can be troubleshooted:
- Checking the quality of inputs: Ensure that the PDF scanning resolution ≥ 300DPI, fuzzy documents are recommended to re-scanning
- Validating Model Integrity: Confirm that the OCRFlux-3B model files are complete, especially key files such as vocab.json
- Adjustment of processing parameters: For documents with special fonts, try adjusting the text recognition thresholds in the model
- segmentation: Convert and merge very large documents in chapters.
Advanced Solutions:
- When submitting an issue via GitHub, include sample documentation and a screenshot of the error.
- For domain-specific documents (e.g., medical papers), consider fine-tune models
- Check memory usage in Docker logs and increase container memory allocation if necessary
For community support, the project team promises to respond to critical issues within 48 hours. For complex layout issues, it is recommended to use the tool's built-in layout debug mode to generate analysis reports.
This answer comes from the articleOCRFlux: Lightweight tool for converting PDFs and images to MarkdownThe
May not be reproduced without permission:AI productivity tools " How should I troubleshoot and resolve recognition errors when using OCRFlux?
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