将图像质量评估模型适配到文档领域，实现高精度文档质量打分。
DeQA-Doc: Adapting DeQA-Score to Document Image Quality Assessment
- 利用多模态大模型与软标签策略回归连续文档质量分。
- 在多种退化类型下超越基线，性能显著提升。
- 适合需要自动评估扫描文档质量的场景，如档案数字化。
文档质量评估对文档数字化、光学字符识别和档案保存等应用至关重要。现有方法常难以提供准确且鲁棒的质量评分，限制了实际应用。随着多模态大模型（MLLM）的快速发展，基于MLLM的图像质量评估方法已取得显著进展。本文将这一成果拓展至文档领域，提出DeQA-Doc框架，通过适配最先进的MLLM-based图像质量评分器DeQA-Score，实现文档质量评估。该框架利用MLLM的视觉语言能力，结合软标签策略，回归连续文档质量分数。为适配DeQA-Score，我们采用两种互补方案构建无方差信息的软标签，并放宽分辨率限制以支持高分辨率文档图像。此外，引入集成方法进一步提升性能。大量实验表明，DeQA-Doc在多种退化类型下显著优于现有基线，具备高精度与强泛化能力。代码与模型权重已开源于https://github.com/Junjie-Gao19/DeQA-Doc。
原文摘要 · Abstract (English)
Document quality assessment is critical for a wide range of applications including document digitization, OCR, and archival. However, existing approaches often struggle to provide accurate and robust quality scores, limiting their applicability in practical scenarios. With the rapid progress in Multi-modal Large Language Models (MLLMs), recent MLLM-based methods have achieved remarkable performance in image quality assessment. In this work, we extend this success to the document domain by adapting DeQA-Score, a state-of-the-art MLLM-based image quality scorer, for document quality assessment. We propose DeQA-Doc, a framework that leverages the visual language capabilities of MLLMs and a soft label strategy to regress continuous document quality scores. To adapt DeQA-Score to DeQA-Doc, we adopt two complementary solutions to construct soft labels without the variance information. Also, we relax the resolution constrains to support the large resolution of document images. Finally, we introduce ensemble methods to further enhance the performance. Extensive experiments demonstrate that DeQA-Doc significantly outperforms existing baselines, offering accurate and generalizable document quality assessment across diverse degradation types. Codes and model weights are available in https://github.com/Junjie-Gao19/DeQA-Doc.
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