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DeepNet-WI: a deep-net model for offline Urdu writer identification

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Abstract

Since the dawn of civilization, handwriting has been one of the most important forms of communication. However, as handwriting differs from person to person, writer identification has become a promising application of pattern recognition to identify the actual writer of a handwritten document. Handwriting can be either online or offline, depending on how it was obtained. Users can write directly on tablets, smartphones, touch screens, PDAs, and other devices using input devices with online handwriting, whereas offline handwriting is done with a pen and paper. With the advent of artificial intelligence, and most importantly deep learning techniques, the development of writer identification systems based on offline handwritten documents has gained a lot of attention. Deep learning models have the capability of automatic feature extraction, which results in increased performance. From the literature survey, it was revealed that least attention has been paid towards the development of deep learning-based writer identification systems for offline Urdu handwritten documents, unlike the English and Arabic scripts. Therefore, in this paper, we proposed an offline Urdu handwritten writer identification system using a deep learning model inspired by the VGG-16 model of CNN. The model was trained and tested on a novel Urdu handwritten dataset contributed by 318 distinct Urdu writers, resulting in an overall training accuracy of 98.71% and a testing accuracy of 99.11%. The results achieved showed that the proposed model outperformed the already existing writer identification techniques.

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Dataset availability statement

Public dataset of Urdu script is not available for the experimental work. So, the authors have generated their own corpus for the experimental work.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, GZS Campus College of Engineering and Technology, Maharaja Ranjit Singh Punjab Technical University, Bathinda, Punjab, 151001, India

    Syed Tufael Nabi & Paramjeet Singh

  2. Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University, Bathinda, Punjab, 151001, India

    Munish Kumar

Authors
  1. Syed Tufael Nabi
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  2. Munish Kumar
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  3. Paramjeet Singh
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Correspondence to Munish Kumar.

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Nabi, S.T., Kumar, M. & Singh, P. DeepNet-WI: a deep-net model for offline Urdu writer identification. Evolving Systems (2023). https://doi.org/10.1007/s12530-023-09504-1

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