Abstract
Document image quality assessment (DIQA) is an important component for various applications, including optical character recognition (OCR), document restoration, and the evaluation of document image processing systems. In this paper, we introduce a subjective DIQA dataset DIQA-5000. The DIQA-5000 dataset comprises 5,000 document images, generated by applying multiple document enhancement techniques to 500 real-world images with diverse distortions. Each enhanced image was rated by 15 subjects across three rating dimensions: overall quality, sharpness, and color fidelity. Furthermore, we propose a specialized no-reference DIQA model that exploits document layout features to maintain quality perception at reduced resolutions to lower computational cost. Recognizing that image quality is influenced by both low-level and high-level visual features, we designed a feature fusion module to extract and integrate multi-level features from document images. To generate multi-dimensional scores, our model employs independent quality heads for each dimension to predict score distributions, allowing it to learn distinct aspects of document image quality. Experimental results demonstrate that our method outperforms current state-of-the-art general-purpose IQA models on both DIQA-5000 and an additional document image dataset focused on OCR accuracy.
Abstract (translated)
文档图像质量评估（DIQA）是包括光学字符识别（OCR）、文档恢复以及文档图像处理系统评价等应用中的一个重要组成部分。本文介绍了一个主观的DIQA数据集——DIQA-5000。该数据集中包含5,000张通过多种增强技术生成、基于500张具有不同失真的真实世界图片制作而成的文档图像。每个增强后的图像都由15位参与者在三个评价维度上进行评分：整体质量、清晰度和色彩保真度。 此外，我们提出了一种专门针对无参考DIQA模型的方法，该方法利用文档布局特征，在降低计算成本的同时保持低分辨率下的质量感知能力。考虑到图像质量受到视觉层面（包括低级和高级）特征的影响，我们设计了一个特性融合模块来从文档图像中提取并整合多层级的特征。 为了生成多维度评分，我们的模型为每个维度设定了独立的质量头部以预测评分分布，这使它能够学习到文档图像质量的不同方面。实验结果表明，在DIQA-5000数据集和一个专注于OCR准确度的额外文档图像数据集上，我们提出的方法优于当前最先进的通用IQA（Image Quality Assessment）模型。
URL
https://arxiv.org/abs/2509.17012
