# GlotOCR Bench：OCR 模型仍难以应对少数之外的 Unicode 文字

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-04-14 08:00
- AIHOT 链接：https://aihot.virxact.com/items/cmo0835a6007msli2a1z67yox
- 原文链接：https://arxiv.org/abs/2604.12978

## AI 摘要

研究团队发布涵盖 100 余种 Unicode 文字的 GlotOCR Bench 基准测试，包含干净与退化图像变体。评估显示，多数视觉语言模型仅能正确处理不到 10 种文字，最强前沿模型也难以泛化至 30 种以上。性能与文字级预训练覆盖率高度相关，面对陌生文字时模型会产生随机噪声或幻觉已知相似字符。该基准测试及渲染流程已开源。

## 正文

Optical character recognition (OCR) has advanced rapidly with the rise of vision-language models, yet evaluation has remained concentrated on a small cluster of high- and mid-resource scripts. We introduce GlotOCR Bench, a comprehensive benchmark evaluating OCR generalization across 100+ Unicode scripts. Our benchmark comprises clean and degraded image variants rendered from real multilingual texts. Images are rendered using fonts from the Google Fonts repository, shaped with HarfBuzz and rasterized with FreeType, supporting both LTR and RTL scripts. Samples of rendered images were manually reviewed to verify correct rendering across all scripts. We evaluate a broad suite of open-weight and proprietary vision-language models and find that most perform well on fewer than ten scripts, and even the strongest frontier models fail to generalize beyond thirty scripts. Performance broadly tracks script-level pretraining coverage, suggesting that current OCR systems rely on language model pretraining as much as on visual recognition. Models confronted with unfamiliar scripts either produce random noise or hallucinate characters from similar scripts they already know. We release the benchmark and pipeline for reproducibility. Pipeline Code: https://github.com/cisnlp/glotocr-bench, Benchmark: https://hf.co/datasets/cis-lmu/glotocr-bench.