Limitations and Solutions
As an open source OCR tool, RolmOCR suffers from the following technical boundaries:
- Low-quality document processing: For fuzzy/low-contrast documents (e.g. faxes), it is recommended to use OpenCV first:
- Adaptive Histogram Equalization
- Non-local mean denoising
- Gamma correction (1.2-1.5)
- Complex Table Recognition: For borderless tables, pre-process with Tabula or switch to Reducto's commercial API for fully structured data with bounding boxes.
- Professional Symbol Recognition: Math formulas/chemical equations need to be used with specialized tools such as Mathpix. Solution Path:
- Establishment of a dictionary of specialized terms
- Fine-tuning models to add domain-specific data
The development team suggests that for critical business scenarios, a hybrid workflow of 'RolmOCR preliminary processing + manual verification' should be used to balance efficiency and accuracy. Community users can submit issue to get optimization suggestions for specific scenarios.
This answer comes from the articleRolmOCR: Document OCR Model for Recognizing Handwritten and Slanted CharactersThe
May not be reproduced without permission:AI productivity tools " What are the technical limitations of RolmOCR? How to cope with it?
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