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arXiv · 2603.19790

Geometric Risk Control for Vision-Language Model OCR

Weile Gong·Zijian Lu·Mingcai Chen·Yiping Zuo·Xin He·Weibei Fan
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Abstract

Vision-language models (VLMs) enable flexible generative optical character recognition (OCR), while their open-ended decoders can expose wrong but fluent text with weak visual support. In audit-sensitive records, such an output can be more costly than abstention. Frozen or externally served VLMs therefore require an external decision layer that can determine whether a transcription has sufficient visual evidence for release. We introduce the Geometric Risk Controller (GRC), a model-agnostic controller that treats controlled geometric transformations as repeatable black-box probes, screens structurally implausible continuations, and releases the unique candidate supported by coherent cross-view evidence. The protocol provides empirical selective exposure control with explicit coverage and query cost under a reproducible fixed decision rule. Experiments across frozen VLMs and standard scene-text benchmarks consistently reduce mean, upper-tail, and catastrophic error among released outputs while retaining high coverage.

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Weile Gong, Zijian Lu, Mingcai Chen, Yiping Zuo, Xin He, Weibei Fan. 2026-03-20. Geometric Risk Control for Vision-Language Model OCR. https://arxiv.org/abs/2603.19790

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