Balochi Non Cursive Isolated Character Recognition using Deep Neural Network

The text recognition research in artificial intelli-gence has enabled machines not only to recognize the human spoken languages but also to interpret them. Optical character recognition is a subarea of AI that converts scanned text images into an editable document. The researchers proposed various text recognition techniques to identify cursive and connected scripts written from left to right but their correct recognition is still a challenging problem for the visual methods. The Balochi language is one of them spoken by a significant part of the world population and no research conducted on the recognition this regional language of Pakistan. In this paper, we propose a convolutional neural network based model for Balochi script recognition for non-cursive characters. Our model optimized small VGGNet model and achieved exceptional precision and speed over the state of the art methods of machine learning. We experimented and compared the proposed method with the baseline LeNet model, the results showed the proposed method improved over the baseline method with a precision of 96%. We additionally collected and processed the Balochi characters dataset and made it public to carry further research in the future.

@article{Naseer2020,
  title     = {Balochi Non Cursive Isolated Character Recognition using Deep Neural Network},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {4},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Ghulam Jan Naseer and Abdul Basit and Imran Ali and Arif Iqbal},
  doi       = {10.14569/IJACSA.2020.0110492},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110492}
}