# Performance Gap Analysis between Latin and Arabic Scripts HTR

arXiv: https://arxiv.org/abs/2606.18884 · HTML: https://mlsanity.com/paper/2606.18884/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 17 Jun 2026.

## Builds on (most cited first, 11 of 11)

- [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://mlsanity.com/paper/2109.10282/) (2021)
- [HTR-VT: Handwritten Text Recognition with Vision Transformer](https://mlsanity.com/paper/2409.08573/) (2024)
- [Best Practices for a Handwritten Text Recognition System](https://mlsanity.com/paper/2404.11339/) (2024)
- [Muharaf: Manuscripts of Handwritten Arabic Dataset for Cursive Text Recognition](https://mlsanity.com/paper/2406.09630/) (2024)
- [Handwritten Text Recognition: A Survey](https://mlsanity.com/paper/2502.08417/) (2025)
- [HATFormer: Historic Handwritten Arabic Text Recognition with Transformers](https://mlsanity.com/paper/2410.02179/) (2024)
- [Advancements and Challenges in Arabic Optical Character Recognition: A Comprehensive Survey](https://mlsanity.com/paper/2312.11812/) (2023)
- [Meta-DAN: towards an efficient prediction strategy for page-level handwritten text recognition](https://mlsanity.com/paper/2504.03349/) (2025)
- [A Human-in-the-Loop Label Error Detection Framework Applied to Arabic-Script HTR Datasets](https://mlsanity.com/paper/2601.16713/) (2026)
- [Cross-Lingual Learning within Arabic Script for Low-Resource HTR](https://mlsanity.com/paper/2605.02089/) (2026)
- [Understanding Cross-Language Transfer Improvements in Low-Resource HTR: The Role of Sequence Modeling](https://mlsanity.com/paper/2605.05900/) (2026)

## Abstract (arXiv)

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.