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:
this https URL

通过提供 1000 多幅自然场景图像，该研究提出了一个新的多任务乌尔都语场景文本数据集，用于文本检测、识别和视觉问答任务，并解决了之前数据集在面对任意形状文本时的限制。该数据集通过引入额外的标注点，方便了对能够处理多样文本布局、复杂形状和非标准方向的方法进行开发和评估，帮助进一步推动乌尔都语场景文本理解方法的发展。