A Holistic Approach to Urdu Language Word Recognition using Deep Neural Networks

Authors

  • H. R. Khan Department of Electronic Engineering, NED University of Engineering and Technology, Pakistan
  • M. A. Hasan Department of Bio-Medical Engineering, NED University of Engineering and Technology, Pakistan
  • M. Kazmi Faculty of Electrical and Computer Engineering, NED University of Engineering & Technology, Pakistan
  • N. Fayyaz Department of Electrical Engineering, NED University of Engineering and Technology, Pakistan
  • H. Khalid Department of Computer and Information Systems Engineering, NED University of Engineering and Technology, Pakistan
  • S. A. Qazi Department of Computer and Information Systems Engineering, NED University of Engineering and Technology, Pakistan

Abstract

Urdu is one of the most popular languages in the world. It is a Persianized standard register of the Hindi language with considerable and valuable literature. While digital libraries are constantly replacing conventional libraries, a vast amount of Urdu literature is still handwritten. Digitizing this handwritten literature is essential to preserve it and make it more accessible. Nevertheless, the scarcity of Urdu Optical Character Recognition (OCR) research limits a digital library's scope to a manual document search. The limited research work in this area is mainly due to the complexity of Urdu Script. Unlike the English language, the Urdu writing style is cursive, bidirectional, and character shapes and sizes highly vary depending on their position. Holistic word recognition is found to be a better solution among many other text segmentation techniques as it takes the complete word into account instead of segmenting it explicitly or implicitly. For this project, the data of five different Urdu words were collected for training and testing a convolutional neural network and 96% recognition accuracy was achieved.

Keywords:

word recognition, Urdu, deep learning, cursive writting

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How to Cite

[1]
H. R. Khan, M. A. Hasan, M. Kazmi, N. Fayyaz, H. Khalid, and S. A. Qazi, “A Holistic Approach to Urdu Language Word Recognition using Deep Neural Networks”, Eng. Technol. Appl. Sci. Res., vol. 11, no. 3, pp. 7140–7145, Jun. 2021.

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