Performance Gap Analysis between Latin and Arabic Scripts HTR

2026-06-17

AI Summary

This study investigates the fundamental causes underlying the performance gap between Arabic and Latin handwritten text recognition (HTR). Using a unified CRNN architecture, the authors systematically compare the two scripts across nine datasets under varying training scales and, for the first time under controlled conditions, quantify the contributions of data quality, character distribution, and visual variability. Findings reveal that even with full training data, Arabic HTR exhibits a 5–7% higher character error rate than Latin; data cleaning partially mitigates but does not eliminate this gap. Approximately 30% of Arabic substitution errors stem from confusable visually similar characters—substantially higher than the 15% observed for Latin. These results indicate that Arabic script’s high visual variability and heavy-tailed character distribution necessitate significantly more high-quality data to achieve recognition performance comparable to Latin.

Abstract

Recent studies have shown that handwritten text recognition (HTR) systems perform worse on Arabic-script datasets than on Latin-script data. However, the reasons for this gap are still not well understood due to the lack of controlled comparisons. In this work, we present a comprehensive study of Arabic and Latin scripts HTR using a unified CRNN model for line-level HTR across nine datasets (including KHATT (Arabic), Muharaf (Arabic), NUST-UHWR (Urdu), PHTD (Persian), IAM (English), READ-2016 (German), and others) and di ferent training sizes (K in {100, 500, 1000, 2000, ..., Kfull}). Our results show the performance gap remains: it is large in low-resource settings, decreases with more data, but remains even at full scale, with a consistent difference of 5-7 CER points. We show that annotation quality matters, as many datasets contain labeling errors. Cleaning reduces error rates and narrows the gap, but does not eliminate it. In addition, we find that a fixed number of training samples provides less effective coverage in Arabic due to higher visual variability, requiring more data to learn similar representations. We compare recognition across datasets in terms of the number of text lines and the number of characters, showing an equivalence trade-off. We compare character frequency distributions across scripts and show that Arabic is significantly more heavy-tailed than Latin. Our error analysis reveals that around 30 percent of substitution errors in Arabic datasets (e.g., KHATT) are caused by confusion between visually similar characters, compared to about 15 percent in Latin-script datasets such as IAM.

Problem

• Handwritten Text Recognition
• Performance Gap
• Arabic Script
• Latin Script
• Character Confusion

Innovation

• handwritten text recognition
• script comparison
• CRNN
• character error rate
• visual variability