# Cross-Lingual Learning within Arabic Script for Low-Resource HTR

arXiv: https://arxiv.org/abs/2605.02089 · HTML: https://mlsanity.com/paper/2605.02089/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 3 May 2026.

## Builds on (most cited first, 10 of 10)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Learn to Augment: Joint Data Augmentation and Network Optimization for Text Recognition](https://mlsanity.com/paper/2003.06606/) (2020)
- [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)
- [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)
- [Task Grouping for Multilingual Text Recognition](https://mlsanity.com/paper/2210.07423/) (2022)
- [A Human-in-the-Loop Label Error Detection Framework Applied to Arabic-Script HTR Datasets](https://mlsanity.com/paper/2601.16713/) (2026)
- [CALText: Contextual Attention Localization for Offline Handwritten Text](https://mlsanity.com/paper/2111.03952/) (2021)

## Built on by (most cited first, 2 of 2)

- [Understanding Cross-Language Transfer Improvements in Low-Resource HTR: The Role of Sequence Modeling](https://mlsanity.com/paper/2605.05900/) (2026)
- [Performance Gap Analysis between Latin and Arabic Scripts HTR](https://mlsanity.com/paper/2606.18884/) (2026)

## Abstract (arXiv)

Handwritten Text Recognition (HTR) with limited labeled data remains a challenging problem, particularly for Arabic-script languages. Although modern sequence-based recognizers perform well in high-resource settings, their accuracy degrades sharply as training data becomes scarce. Arabic-script languages share a common writing system with substantial character overlap, motivating cross-lingual learning as a strategy to mitigate data scarcity. We conduct a controlled line-level study of cross-lingual joint training for Arabic-script HTR under low-resource regimes (number of samples K = 100, 500, 1000 labeled lines) on Arabic (KHATT), Urdu (NUST-UHWR) and Persian (PHTD). CRNN and Vision Transformer-based HTR-VT models are trained on the union of multiple related Arabic-script datasets to mitigate the data scarcity and are evaluated on individual target languages. Both architectures benefit from cross-language training under low-resource conditions. CRNN remains more effective under extremely limited target-language data, whereas the benefits of cross-language training for HTR-VT become less consistent as larger amounts of target-language data become available. On Persian (PHTD), joint training achieves a Character Error Rate (CER) of 9.99 , surpassing previously reported results despite not using the full available training data. On an additional Urdu dataset (UNHD), joint training reduces CER from 17.20 to 14.45.