The Reducto AI team improves the recognition performance of RolmOCR through two main strategies: data enhancement and model tuning. The technical solution contains:
- Training dataset contains 151 TP3T of rotated samples for enhanced tilt adaptation
- Handwriting samples from 20% improve recognition of unconventional fonts
- Reinforcing Character Distinction Using Contrastive Learning Loss Functions
- A cross-modal pretraining architecture based on Qwen2.5-VL
These optimizations result in significant performance improvements:
- Reduced handwriting recognition error rate compared to the base model 37%
- 28 percentage point improvement in word-level accuracy for skewed documents
- Text Extraction Success Rate in Complex Contexts Breaks 90%
Practical applications have proven that the solution performs well in the following scenarios: processing scanned copies of academic papers, digitizing historical archives, and recognizing multilingual documents with mixed typesetting. The team will continue to optimize the model performance through data iteration.
This answer comes from the articleRolmOCR: Document OCR Model for Recognizing Handwritten and Slanted CharactersThe
May not be reproduced without permission:AI productivity tools " RolmOCR-optimized training strategy to improve recognition rate of complex scenes
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