HTR systems show persistent performance gap for Arabic vs. Latin scripts

A new study published on arXiv analyzes the performance gap between handwritten text recognition (HTR) systems for Latin and Arabic scripts. Researchers used a unified Convolutional Recurrent Neural Network (CRNN) model across multiple datasets and training sizes to compare performance. The findings indicate a persistent performance gap, with Arabic scripts showing higher error rates, especially in low-resource settings. This gap narrows with increased data but remains even at full scale, attributed partly to annotation quality issues and the higher visual variability and heavy-tailed character frequency distributions in Arabic. AI

IMPACT Highlights challenges in HTR for non-Latin scripts, suggesting a need for more data and better annotation quality for equitable performance.

RANK_REASON Academic paper detailing a performance gap analysis in HTR systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• Convolutional Recurrent Neural Network
• IAM
• KHATT
• Muharaf
• NUST-UHWR
• READ-2016
• Sana Sabah Sabry Al-Azzawi

• paper
• other

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