Researchers have developed LayoutLite, a novel module designed to enhance the efficiency of optical character recognition (OCR) systems that utilize vision-language models. This plug-and-play module operates by performing implicit layout analysis at the token level, allowing it to identify and remove low-information visual tokens before they reach the language decoder. By employing a reinforcement learning approach for training without human annotations, LayoutLite can significantly reduce the number of visual tokens and inference costs, as demonstrated by experiments on OmniDocBench and specialized OCR models like FireRed-OCR and Logics-Parsing-V2, achieving over 40% reduction in latency and FLOPs with minimal impact on recognition quality. AI
IMPACT Accelerates VLM-based OCR systems by reducing token length and inference costs.
RANK_REASON Academic paper detailing a new method for improving OCR efficiency. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- FireRed-OCR
- LayoutLite
- Logics-Parsing-V2
- OmniDocBench
- optical character recognition
- Vision--Language Models
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