构建首个真实场景的OCR质量评估数据集，支持模型训练与评测。
OCR-Quality: A Human-Annotated Dataset for OCR Quality Assessment
- 1000页真实文档转为300DPI图片，涵盖多类型文本。
- 用视觉语言模型预处理后人工打分，4级质量标准更精准。
- 适合做OCR质量检测、验证系统开发的研究者使用。
我们提出OCR-Quality，一个用于评估和开发OCR质量评估方法的全面人工标注数据集。该数据集包含1000张从学术论文、教科书、电子书及多语言文档中采样的PDF页面转换成的300 DPI PNG图像，覆盖多样真实场景。每份文档经先进视觉-语言模型（VLMs）处理后，由人工依据四级评分体系（1：优秀，2：良好，3：一般，4：差）标注质量得分。数据集提供详细来源信息、标注指南及多种难度代表性案例。OCR-Quality填补了真实应用场景下可靠OCR质量评估的空白，为训练和评估OCR验证系统提供了重要基准。数据集已公开于https://huggingface.co/datasets/Aslan-mingye/OCR-Quality。
原文摘要 · Abstract (English)
We present OCR-Quality, a comprehensive human-annotated dataset designed for evaluating and developing OCR quality assessment methods. The dataset consists of 1,000 PDF pages converted to PNG images at 300 DPI, sampled from diverse real-world scenarios, including academic papers, textbooks, e-books, and multilingual documents. Each document has been processed using state-of-the-art Vision-Language Models (VLMs) and manually annotated with quality scores using a 4-level scoring system (1: Excellent, 2: Good, 3: Fair, 4: Poor). The dataset includes detailed source information, annotation guidelines, and representative cases across various difficulty levels. OCR-Quality addresses the critical need for reliable OCR quality assessment in real-world applications and provides a valuable benchmark for training and evaluating OCR verification systems. The dataset is publicly available at https://huggingface.co/datasets/Aslan-mingye/OCR-Quality .
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