Researchers have developed DocOCR-Eval, a new framework designed to evaluate and select optical character recognition (OCR) tools for document understanding tasks without requiring ground truth annotations. This framework uses a three-stage correction and ranking strategy to approximate annotation-based ordering, proving effective even in label-scarce scenarios. The study demonstrates that aggregating results from multiple multimodal large language models (MLLMs) enhances alignment with annotation-based rankings, offering practical guidance for deploying document parsing systems. AI
IMPACT Provides a practical method for selecting optimal OCR tools, potentially improving efficiency in document understanding tasks.
RANK_REASON Academic paper detailing a new evaluation framework for OCR tools. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- CatalyzeX
- DagsHub
- DocOCR-Eval
- Gotit.pub
- Hugging Face
- IArxiv
- MLLMs
- optical character recognition
- ScienceCast
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