Cursive-Text: A Comprehensive Dataset for End-to-End Urdu Text Recognition in Natural Scene Images

Published: 18 June 2020| Version 3 | DOI: 10.17632/k5fz57zd9z.3
Asghar Ali


We present a comprehensive dataset for Urdu text detection and recognition in natural scene images. To develop the dataset, more than 2500 natural scene images were captured with a digital camera and a built-in mobile phone camera. Three separate datasets for isolated Urdu character images, cropped word images and end-to-end text spotting are developed. The isolated Urdu character image dataset contains 19901 images, while the cropped word image dataset contains 14100 cropped words. A lexicon of more than 40K commonly used Urdu words is also created. The ground truths for each of the image in the isolated character, cropped word or text spotting datasets are provided separately. The proposed datasets can be used to perform Urdu text detection, recognition or end-to-end recognition in natural scenes. These datasets can also be helpful to develop Arabic and Persian natural scene text detection and recognition systems, as Urdu is a derived language of these scripts and has many similar letters. The datasets can also be helpful to develop multi-language translation systems.



Computer Vision, Pattern Recognition, Scene Understanding