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PHND: Pashtu Handwritten Numerals Database and deep learning benchmark

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  • Khalil Khan
  • Byeong-hee Roh
  • Jehad Ali
  • Rehan Ullah Khan
  • Irfan Uddin
  • Saqlain Hassan
  • Rabia Riaz
  • Nasir Ahmad

Abstract

In this paper we introduce a real Pashtu handwritten numerals dataset (PHND) having 50,000 scanned images and make publicly available for research and scientific use. Although more than fifty million people in the world use this language for written and oral communication, no significant efforts are devoted to the Pashtu Optical Character Recognition (POCR). We present a new approach for Pahstu handwritten numerals recognition (PHNR) based on deep neural networks. We train Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) on high-frequency numerals for feature extraction and classification. We evaluated the performance of the proposed algorithm on the newly introduced Pashtu handwritten numerals database PHND and Bangla language number database CMATERDB 3.1.1. We obtained best recognition rate of 98.00% and 98.64% on PHND and CMATERDB 3.1.1. respectively.

Suggested Citation

  • Khalil Khan & Byeong-hee Roh & Jehad Ali & Rehan Ullah Khan & Irfan Uddin & Saqlain Hassan & Rabia Riaz & Nasir Ahmad, 2020. "PHND: Pashtu Handwritten Numerals Database and deep learning benchmark," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-19, September.
    • Handle: RePEc:plo:pone00:0238423
    DOI: 10.1371/journal.pone.0238423
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    References listed on IDEAS

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    1. Riaz Ahmad & Saeeda Naz & Muhammad Zeshan Afzal & Sayed Hassan Amin & Thomas Breuel, 2015. "Robust Optical Recognition of Cursive Pashto Script Using Scale, Rotation and Location Invariant Approach," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-16, September.
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