[论文] HANS: A Handwritten Answer Sheet Dataset for Noisy Hybrid Document Par...

论文概要

研究领域: CV 作者: Xiazhen Wu, Wansong Qin, Yangbin Zheng, Liangda Fang, Zhan Li, Xiujie Huang, Liushen Zhou, Quanlong Guan 发布时间: 2026-10-08 arXiv: 2610.12363

中文摘要

智能批改与自动评分技术是智慧教育的关键基础设施，但现有文档解析和手写识别基准多为结构良好的印刷文档或孤立数学表达式设计，缺少捕捉学生答卷复杂特性的数据集——包括多行推导过程、文本与数学公式的异构混合、涂改删除等噪声痕迹。为填补空白，我们提出 HANS：首个面向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、手绘表格和涂改、删除等多样噪声模式，附带细粒度标注，为鲁棒识别研究奠定可靠基础。在 HANS 之上我们提出 NA-GOT：端到端框架，通过特征级轻量噪声抑制模块与解码阶段融入的噪声感知注意力机制实现两级噪声抑制。实验表明 HANS 对现有方法构成重大挑战，而 NA-GOT 在答卷过程识别的准确性和稳定性上均取得显著提升。数据集将在发表后公开。

原文摘要

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, accompanie...

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