Cross-Language Learning within Arabic Script for Low-Resource HTR

TL;DR

This paper explores cross-script transfer learning for Arabic-script handwritten text recognition, demonstrating that shared characters drive transfer benefits and providing new state-of-the-art results in low-resource settings.

Contribution

It introduces a controlled study on cross-script training for Arabic-script HTR, highlighting the importance of shared characters and releasing code and data for reproducibility.

Findings

Cross-script transfer improves recognition accuracy in low-resource Arabic-script HTR.

Shared characters across scripts are key to transfer success.

State-of-the-art results achieved on Persian and Urdu datasets with joint training.

Abstract

Handwritten Text Recognition (HTR) under 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-script training as a strategy to mitigate data scarcity. We performed experiments on Arabic, Urdu, and Persian scripts and achieved improvements over single-script baselines (new SotA especially for low-resource settings). A key finding of our experiments is that cross-script transfer is largely driven by script-level overlap rather than uniform accuracy improvements. Through a statistical character-level analysis we show that gains are structurally concentrated on characters shared across scripts, while language-specific characters exhibit limited or negative transfer. These findings provide insight into transfer dynamics in low-resource script families. Detailed results include: We conduct a controlled line-level study of cross-script joint training for Arabic-script HTR under low-resource regimes (number of samples K \in 100, 500, 1000 labeled lines) on Arabic (KHATT), Urdu (NUST-UHWR), and Persian (PHTD). A CRNN model is trained on the union of multiple related Arabic-script datasets and evaluated on individual target languages. 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 Urdu dataset (UNHD), joint training reduces CER from 17.20 to 14.45. Code and data splits are released to ensure reproducibility.1