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

Asghar Ali Chandio^1,2, Md Asikuzzaman¹, Mark Pickering¹

¹School of Engineering and Information Technology, University of New South Wales, Canberra, Australia.

|June 4, 2020

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This summary is machine-generated.

This study introduces a novel dataset for Urdu text detection and recognition in natural scenes. It enables advancements in computer vision and multilingual translation systems.

Area of Science:

Computer Vision
Pattern Recognition
Document Analysis

Background:

Reading text in natural scenes is crucial for computer vision and pattern recognition.
Existing datasets lack comprehensive coverage for Urdu natural scene text.

Purpose of the Study:

To present a comprehensive dataset for Urdu text detection and recognition in natural scene images.
To provide a benchmark for Urdu natural scene text analysis.

Main Methods:

Collected over 2500 natural scene images using digital and mobile cameras.
Developed three datasets: isolated Urdu characters, cropped words, and end-to-end text spotting.
Utilized state-of-the-art machine learning and deep neural networks for evaluation.

Keywords:

Convolutional neural networks Cursive text in the wild Multilingual text spotting dataset Natural scene images Urdu natural scene text dataset Urdu text detection Urdu text recognition

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Main Results:

The dataset includes isolated characters, cropped words, and text spotting instances.
Achieved high classification accuracies using advanced deep neural networks.
The dataset is the first of its kind for Urdu natural scene text detection and recognition.

Conclusions:

The proposed dataset facilitates Urdu text detection and recognition research.
It can aid in developing Arabic, Persian, and multilingual translation systems.
This work establishes a benchmark for document analysis and recognition in natural scenes.