Many historical handwritten records in low-resource languages remain difficult to access through modern digital systems. This limits efforts to preserve and study cultural heritage at scale. Chagatai manuscripts exemplify these challenges within the Eastern Turki tradition. For centuries, it served as a major written language across Central Asia and supported a rich literary tradition. Large collections of Chagatai manuscripts still survive today, yet only a small amount of this material exists in digital form. As the technical literature specifically focused on Chagatai-HTR remains in its nascent stage, this review synthesizes indirect evidence from taxonomically related Perso-Arabic scripts to establish a foundational research framework. This article presents a systematic literature review following the PRISMA guidelines to examine artificial intelligence methods for handwritten text recognition (HTR) and text restoration in low-resource languages. Analyzing 50 studies published between 2020 and 2026, the review categorizes research trends into handwritten text recognition (HTR), optical character recognition (OCR), script classification, dataset development, and multimodal vision–language systems. The findings reveal a significant architectural shift from traditional segmentation-based CNN and RNN models toward transformer architectures and multimodal approaches. However, for Chagatai specifically, the primary obstacle is not the lack of advanced models but a critical scarcity of basic research infrastructure, including expert-verified transcriptions, annotation standards, and open benchmark datasets. Consequently, this article proposes a concrete development roadmap focusing on systematic digitization, expert annotation, transfer learning, and the creation of baseline models to enable reproducible evaluations.

Digitizing the historical Chagatai manuscripts of the Gunnar Jarring Collection is severely hindered by cursive orthographic complexity and extreme data scarcity. This study establishes a rigorous empirical HTR benchmark, comparing two distinct paradigms under identical, highly constrained conditions: a convolutional recurrent neural network (CRNN) trained from scratch and pretrained Vision-Transformer-based architectures (TrOCR Base and Large). To eliminate intra-page data leakage, a strict page-level 80/10/10 partitioning protocol is enforced across the 2531 manually annotated text lines derived from 230 pages. To guarantee statistical reliability, all configurations are evaluated across three independent random seeds. The fine-tuned TrOCR Large model with architecture-specific data augmentation achieved the highest accuracy, yielding a Character Error Rate (CER) of 9.82 ± 0.05% and a Word Error Rate (WER) of 33.27 ± 0.77%, outperforming the from-scratch CRNN baseline (18.07 ± 0.79% CER) by 8.25% absolute. Our decoupled evaluations reveal that while pretraining provides a robust prior that stabilizes learning, spatial augmentation acts as a crucial regularizer. Our decoupled evaluations reveal that the performance advantage of fine-tuned Vision Transformers over scratch-trained CRNNs stems primarily from the stabilizing prior of pretrained transfer learning, while spatial augmentation acts as a crucial regularizer under extreme data scarcity. Character-level error taxonomies identify systemic failure modes, including deletion biases in Transformers and insertion-heavy “diacritic babbling” in connectionist temporal classification models.