HANS：用于含噪混合文档解析的手写答题卡数据集

中文摘要 AI 辅助

智能阅卷与自动评分技术是智慧教育的关键基础设施。然而，现有的文档解析与手写识别基准大多针对结构规整的印刷文档或孤立的数学表达式设计，缺乏能捕捉学生答题卡复杂特征的数据集，这些特征包括多行推导过程、文本与数学公式的混合内容，以及删除线等噪声伪影。为填补这一空白，我们推出HANS，首个专为真实教育场景构建的数据集，涵盖数学表达式、自然语言文本、手绘表格，以及修正、删除等多种噪声模式，并附带细粒度标注，为鲁棒识别研究奠定可靠基础。基于HANS，我们提出NA-GOT，一种端到端框架，通过在特征级运行的轻量噪声抑制模块实现两阶段噪声抑制，同时在解码阶段引入噪声感知注意力机制作为补充。实验结果表明，HANS对现有方法构成了重大挑战，而NA-GOT在答题过程识别的准确性和稳定性上均实现了显著提升。该数据集将在发表后公开提供。

英文摘要

Intelligent grading and automated scoring technologies constitute critical infrastructure for smart education. However, existing document parsing and handwriting recognition benchmarks are predominantly designed for well-structured printed documents or isolated mathematical expressions, lacking datasets that capture the complex characteristics inherent to student answer sheets, including multi-line derivation processes, heterogeneous mixtures of text and mathematical formulae, and noise artifacts such as strikethroughs. To address this gap, we introduce HANS, the first dataset explicitly constructed for real-world educational scenarios, encompassing mathematical expressions, natural language text, hand-drawn tables, and diverse noise patterns including corrections and deletions, accompanied by fine-grained annotations that establish a reliable foundation for robust recognition research. Building upon HANS, we propose NA-GOT, an end-to-end framework that achieves two-stage noise suppression through a lightweight noise suppression module operating at the feature level, complemented by a noiseaware attention mechanism incorporated into the decoding stage. Experimental results demonstrate that HANS poses substantial challenges to existing methods, while NA-GOT achieves significant improvements in both accuracy and stability for answer process recognition. The dataset will be made publicly available upon publication.

发表机构

• Jinan University（暨南大学）

机构由 AI 辅助整理，请以论文原文为准。