  The development of Urdu scene text detection, recognition, and Visual
Question Answering (VQA) technologies is crucial for advancing accessibility,
information retrieval, and linguistic diversity in digital content,
facilitating better understanding and interaction with Urdu-language visual
data. This initiative seeks to bridge the gap between textual and visual
comprehension. We propose a new multi-task Urdu scene text dataset comprising
over 1000 natural scene images, which can be used for text detection,
recognition, and VQA tasks. We provide fine-grained annotations for text
instances, addressing the limitations of previous datasets for facing
arbitrary-shaped texts. By incorporating additional annotation points, this
dataset facilitates the development and assessment of methods that can handle
diverse text layouts, intricate shapes, and non-standard orientations commonly
encountered in real-world scenarios. Besides, the VQA annotations make it the
first benchmark for the Urdu Text VQA method, which can prompt the development
of Urdu scene text understanding. The proposed dataset is available at:
https://github.com/Hiba-MeiRuan/Urdu-VQA-Dataset-/tree/main
