评测大模型对低资源文字的识别能力，发现现有方法仍有明显短板。
Deciphering the Underserved: Benchmarking LLM OCR for Low-Resource Scripts
- 用2520张带多种干扰的图像测试GPT-4o对乌尔都语等语言的识别
- 零样本识别在复杂语言上准确率低，尤其在模糊或小字号时更差
- 呼吁建立标注数据集，推动低资源语言的可访问性研究
本研究评估大型语言模型（如GPT-4o）在低资源脚本（如乌尔都语、阿尔巴尼亚语、塔吉克语）中的光学字符识别（OCR）潜力，以英语为基准。基于包含2,520张图像的精心构建数据集，模拟文本长度、字体大小、背景色和模糊度等现实挑战。结果表明，零样本的LLM OCR在语言结构复杂的脚本中表现受限，凸显了标注数据集与微调模型的必要性。研究强调了弥合文本数字化中语言可及性差距的紧迫性，为实现包容性强健的低资源语言OCR解决方案铺平道路。
原文摘要 · Abstract (English)
This study investigates the potential of Large Language Models (LLMs), particularly GPT-4o, for Optical Character Recognition (OCR) in low-resource scripts such as Urdu, Albanian, and Tajik, with English serving as a benchmark. Using a meticulously curated dataset of 2,520 images incorporating controlled variations in text length, font size, background color, and blur, the research simulates diverse real-world challenges. Results emphasize the limitations of zero-shot LLM-based OCR, particularly for linguistically complex scripts, highlighting the need for annotated datasets and fine-tuned models. This work underscores the urgency of addressing accessibility gaps in text digitization, paving the way for inclusive and robust OCR solutions for underserved languages.
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