Reasoning-OCR：大型多模态模型能否基于OCR线索解决复杂逻辑推理问题？
Reasoning-OCR: Can Large Multimodal Models Solve Complex Logical Reasoning Problems from OCR Cues?
AI总结：
针对现有OCR基准缺乏复杂逻辑推理评估的问题，研究者提出覆盖6类场景、含150道题的Reasoning-OCR基准，测试发现当前LMMs推理性能有待提升，该基准将推动相关研究发展。
摘要：
大型多模态模型（LMMs）的功能日益丰富，同时具备了出色的光学字符识别（OCR）相关能力。现有OCR相关基准主要侧重评估LMMs相对简单的视觉问答、视觉文本解析等能力，但LMMs基于OCR线索处理复杂逻辑推理问题的能力水平仍有待探索。为此，我们提出Reasoning-OCR基准，旨在挑战LMMs基于从丰富视觉文本中提取的线索解决复杂推理问题的能力。Reasoning-OCR覆盖6种视觉场景，包含150道精心设计的问题，分为6类推理挑战，同时尽可能降低了领域专业知识的影响。我们的评估为闭源和开源LMMs在不同推理挑战中的表现提供了洞见，凸显了提升推理性能的紧迫性。我们希望Reasoning-OCR能启发并推动未来基于OCR线索增强复杂推理能力的研究，该基准已在https://github.com/Hxyz-123/ReasoningOCR 公开。
英文摘要：
Large Multimodal Models (LMMs) have become increasingly versatile, accompanied by impressive Optical Character Recognition (OCR) related capabilities. Existing OCR-related benchmarks emphasize evaluating LMMs' abilities of relatively simple visual question answering, visual-text parsing, etc. However, the extent to which LMMs can deal with complex logical reasoning problems based on OCR cues is relatively unexplored. To this end, we introduce the Reasoning-OCR benchmark, which challenges LMMs to solve complex reasoning problems based on the cues that can be extracted from rich visual-text. Reasoning-OCR covers six visual scenarios and encompasses 150 meticulously designed questions categorized into six reasoning challenges. Additionally, Reasoning-OCR minimizes the impact of field-specialized knowledge. Our evaluation offers some insights for proprietary and open-source LMMs in different reasoning challenges, underscoring the urgent to improve the reasoning performance. We hope Reasoning-OCR can inspire and facilitate future research on enhancing complex reasoning ability based on OCR cues. Reasoning-OCR is publicly available at https://github.com/Hxyz-123/ReasoningOCR.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
