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Chinese historical documents are invaluable cultural treasures, but their complexity poses significant comprehension challenges for non-specialists. Despite the success of Large Language Models (LLMs) and Large Vision-Language Models (LVLMs) in modern document tasks, their capabilities in Long (full-book) Historical Do...
DocIntent, a training-free Answerability-Guided Agentic Restoration framework, which first assesses question answerability, then identifies task-relevant degradations and selectively invokes restoration tools, and which consistently improves the average score and consistency of different open- and closed-source MLLMs.
TongGuOCR is proposed, a layout-aware and token-augmented multimodal large language model (MLLM) for OCR of Chinese historical documents that outperforms representative traditional task-specific OCR models, general-purpose MLLMs, and OCR-oriented MLLMs.
This work proposes Single-Patch Text Spotting (SPaTS), a vision-centric framework that routes each text instance through a single anchor visual token and then recovers geometry via full-image refinement and introduces Single-Patch Selective Optimization (SPaSO), a reinforcement learning framework that optimizes discret...
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