Urdu-Text: A Dataset and Benchmark for Urdu Text Detection and Recognition in Natural Scenes

Abstract

Multi-lingual text in natural scene images conveys useful information and is a fundamental tool for tourists to interact with their environment. Multi-lingual text detection and recognition in natural scenes, therefore, has become a challenging problem for researchers in the last few years. Recently, a large-scale multi-lingual dataset for scene text detection and script identification is published by the ICDAR which, contains scene images with text in six different scripts including Arabic. This paper presents a novel dataset and benchmark for Urdu text in natural scenes. Currently, no dataset for Urdu text in natural scenes is publicly available. Urdu is a type of cursive language, which is derived from Arabic script and uses many similar alphabet characters. Therefore, the proposed dataset could be helpful for multi-lingual text detection, recognition and script identification. The aim of this dataset is to help the research community for algorithm development and evaluation of Urdu text in natural scenes. The Urdu-Text dataset contains 1400 complete scene images and 8200-segmented words. The images in the dataset contain a broad variety of text instances in multi-orientations with small and large font sizes. The dataset contains ground truths in the form of bounding boxes at the word level, the script of the text and the text-transcription. The performance of three deep neural networks is evaluated to measure the robustness of the Urdu-Text dataset.