English(EN) A Human-in-the-Loop Label Error Detection Framework Applied to Arabic-Script HTR Datasets

新框架通过AI和人工审核提升阿拉伯文HTR数据集质量

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-16 04:00

研究人员开发了一种新颖的两阶段框架CER-HV，旨在提高用于训练手写文本识别（HTR）模型的数据集质量，特别是针对阿拉伯文脚本语言。该框架结合了卷积循环神经网络（CRNN）进行自动错误检测和“人在回路”验证过程。当应用于阿拉伯文数据集时，CER-HV成功识别了转录和分割错误等标签错误，在数据集清理和模型重新训练后，评估CER提高了多达1.8个百分点。 AI

影响提高了阿拉伯文HTR数据集的质量，可能加速该领域的研发。

排序理由该集群包含一篇详细介绍新框架和实验结果的学术论文，符合研究类别。 [lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CV TIER_1 English(EN) · Sana Al-azzawi, Elisa Barney, Marcus Liwicki · 2026-06-16 04:00

一种人工干预式标签错误检测框架应用于阿拉伯文手写文本识别数据集

arXiv:2601.16713v4 Announce Type: replace Abstract: Despite recent advances, Handwritten Text Recognition (HTR) for Arabic-script languages still lags behind Latin-script HTR. Part of the problem is dataset quality. To help closing this gap, we propose a two-stage framework (CER-…

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一种人工干预式标签错误检测框架应用于阿拉伯文手写文本识别数据集

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