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New framework boosts Arabic HTR dataset quality with AI and human review

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel two-stage framework, CER-HV, designed to improve the quality of datasets used for training Handwritten Text Recognition (HTR) models, particularly for Arabic-script languages. The framework combines a Convolutional Recurrent Neural Network (CRNN) for automated error detection with a human-in-the-loop verification process. When applied to Arabic-script datasets, CER-HV successfully identified label errors such as transcription and segmentation mistakes, leading to an improvement of up to 1.8 percentage points in evaluation CER after dataset cleaning and model retraining. AI

IMPACT Improves dataset quality for Arabic HTR, potentially accelerating research and development in the field.

RANK_REASON The cluster contains an academic paper detailing a new framework and experimental results, fitting the research bucket. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

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

New framework boosts Arabic HTR dataset quality with AI and human review

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The cluster contains an academic paper detailing a new framework and experimental results, fitting the research bucket. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Sana Al-azzawi, Elisa Barney, Marcus Liwicki ·

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

    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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