HANS: A Handwritten Answer Sheet Dataset for Noisy Hybrid Document Parsing

Key points

• Intelligent grading and automated scoring technologies constitute critical infrastructure for smart education.1
• 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.1
• 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.1
• 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.1

Sources (1)

• [1]HANS: A Handwritten Answer Sheet Dataset for Noisy Hybrid Document Parsing
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 05:27 PM

  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.

  Intelligent grading and automated scoring technologies constitute critical infrastructure for smart education.

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