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New HANS dataset targets noisy handwritten answer sheet parsing

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced HANS, a new dataset designed to improve the parsing and recognition of handwritten student answer sheets. This dataset addresses limitations in existing benchmarks by including complex elements like mixed text and formulas, multi-line derivations, and various noise artifacts such as strikethroughs and deletions. To leverage HANS, a novel end-to-end framework called NA-GOT has been proposed, which incorporates a two-stage noise suppression mechanism to enhance accuracy and stability in recognizing handwritten educational content. AI

IMPACT This dataset and framework could advance AI capabilities in educational technology, particularly for automated grading and scoring of complex handwritten student work.

RANK_REASON The item describes a new dataset and a proposed framework for a specific AI research problem, published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New HANS dataset targets noisy handwritten answer sheet parsing

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The item describes a new dataset and a proposed framework for a specific AI research problem, published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Xiazhen Wu, Wansong Qin, Yangbin Zheng, Liangda Fang, Zhan Li, Xiujie Huang, Liushen Zhou, Quanlong Guan ·

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

    arXiv:2610.12363v1 Announce Type: new Abstract: 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 …

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