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

翻译：乌尔都语场景文本检测、识别及视觉问答技术的发展对提升数字内容的可访问性、信息检索能力和语言多样性至关重要，有助于更好地理解和交互乌尔都语视觉数据。本研究致力于弥合文本理解与视觉理解之间的鸿沟。我们提出了一个包含1000余张自然场景图像的多任务乌尔都语场景文本数据集，可用于文本检测、识别及视觉问答任务。我们为文本实例提供了细粒度标注，解决了此前数据集难以处理任意形状文本的局限性。通过增设标注点，该数据集能够促进开发并评估针对真实场景中常见多样化文本布局、复杂形状及非标准朝向的方法。此外，视觉问答标注使其成为乌尔都语文本视觉问答方法的首个基准测试，可推动乌尔都语场景文本理解的发展。该数据集公开于：https://github.com/Hiba-MeiRuan/Urdu-VQA-Dataset-/tree/main