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Equal CER, Unequal Damage: Severity-Weighted Character Error Rate for OCR

Paper recorded by Signals 4 on 2026-10-14 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-14 on arXiv · recorded by Signals 4 on 2026-10-15

Category: cs.CL · 自然语言处理 · first seen 2026-10-15

Abstract

Character Error Rate (CER), the standard metric for OCR and vision-language models (VLMs), charges a typo as much as a wrong name. We introduce semCER, which keeps CER's alignment and denominator and charges each grapheme edit a severity from an LLM judge, with no word segmentation. Normalized CER keeps the score of a whole transcription, where it tracks readers as well as semCER or better, and se

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#247 most recent of 660 cs.CL papers we have recorded · ↑ newer: ChronoCulture: Evaluating Cultural Awareness Across Time in Large Lang · ↓ older: MolLean: A Process-Verifiable Language for Chemical Reasoning
Cite this page: Equal CER, Unequal Damage: Severity-Weighted Character Error Rate for OCR: the #247 most recent of 660 cs.CL papers we have recorded (as of 2026-10-14). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/equal-cer-unequal-damage-severity-weighted-character-error-rate-for-ocr.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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