用熵值热图定位大模型文字识别错误，效果显著。
Entropy Heat-Mapping: Localizing GPT-Based OCR Errors with Sliding-Window Shannon Analysis
- 通过滑动窗口分析每个词的香农熵，生成不确定性热图。
- 大部分真实识别错误都集中在高熵区域，准确率高。
- 适合需要快速修正大模型OCR结果的研究者使用。
视觉语言模型如 OpenAI GPT-4o 能直接从图像中转录数学文档，但其词级别的置信度信号很少被用于定位局部识别错误。我们提出一种熵值热图概念验证方法，将每个词的香农熵转化为可视化“不确定性景观”。通过固定长度滑动窗口扫描熵序列，识别出可能包含缺失符号、括号错配或乱码文本的热点区域。基于一组经筛选的扫描研究页（多分辨率），我们将这些热点与 GPT-4o 实际产生的转录错误进行比对，结果显示绝大多数真实错误确实集中于高熵区域。本研究在无需复杂工程设计的前提下，证明了滑动窗口熵分析可作为后编辑 GPT 基础 OCR 的实用、轻量级辅助工具。所有代码与标注指南已公开，以促进复现与进一步研究。
原文摘要 · Abstract (English)
Vision-language models such as OpenAI GPT-4o can transcribe mathematical documents directly from images, yet their token-level confidence signals are seldom used to pinpoint local recognition mistakes. We present an entropy-heat-mapping proof-of-concept that turns per-token Shannon entropy into a visual ''uncertainty landscape''. By scanning the entropy sequence with a fixed-length sliding window, we obtain hotspots that are likely to contain OCR errors such as missing symbols, mismatched braces, or garbled prose. Using a small, curated set of scanned research pages rendered at several resolutions, we compare the highlighted hotspots with the actual transcription errors produced by GPT-4o. Our analysis shows that the vast majority of true errors are indeed concentrated inside the high-entropy regions. This study demonstrates--in a minimally engineered setting--that sliding-window entropy can serve as a practical, lightweight aid for post-editing GPT-based OCR. All code and annotation guidelines are released to encourage replication and further research.
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