A Survey on Urdu Handwritten Text Recognition: State of the Art, Challenges, and Future Directions

Urdu handwritten text recognition (HTR) has emerged as an important research area in computer vision and natural language processing, with applications in digital archiving, historical manuscript preservation, automated document processing, and assistive technologies. Despite significant progress in machine learning and deep learning, Urdu HTR continues to pose challenges due to the script’s cursive writing style, complex ligatures, diverse character shapes, and the frequent use of diacritics. This paper presents a comprehensive survey of Urdu HTR, covering traditional approaches such as rule-based and machine learning methods, as well as state-of-the-art deep learning architectures. We review publicly available datasets, examine major challenges including handwriting variability and the scarcity of large, annotated resources, and discuss recent trends such as transformer-based models, self-supervised learning, and multimodal recognition frameworks. Finally, we outline promising research directions aimed at advancing Urdu HTR, with an emphasis on developing large-scale datasets, enhancing model robustness and generalization, and enabling deployment in real-world applications.