Pashtu Numerals Recognition through Convolutional Neural Networks

Khalil Khan; Muhammad Waheed; Adeel Shams; Daniyal Munir; Bilawal Shah

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Pashtu Numerals Recognition through Convolutional Neural Networks

Author(s)

Khalil Khan Department of Electrical Engineering, University of Azad Jammu and Kashmir, Pakistan

Muhammad Waheed Department of Electrical Engineering, University of Azad Jammu and Kashmir, Pakistan

Adeel Shams Department of Electrical Engineering, University of Azad Jammu and Kashmir, Pakistan

Daniyal Munir Department of Electrical Engineering, University of Azad Jammu and Kashmir, Pakistan

Bilawal Shah Department of Electrical Engineering, University of Azad Jammu and Kashmir, Pakistan

Abstract

In the proposed paper we introduce a new Pashtu numerals dataset having handwritten scanned images. We make the dataset publically available for scientific and research use. Pashtu language is used by more than fifty million people both for oral and written communication, but still no efforts are devoted to the Optical Character Recognition (OCR) system for Pashtu language. We introduce a new method for handwritten numerals recognition of Pashtu language through the deep learning based models. We use convolutional neural networks (CNNs) both for features extraction and classification tasks. We assess the performance of the proposed CNNs based model and obtained recognition accuracy of 91.45%.

Publication Details

Page(s) 91-96

DOI DOI not available

Published Journal: Journal of Applied and Emerging Sciences , Volume: 9, Issue: 2, Year: 2019

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Keywords

Convolutional Neural Networks, Optical character recognition, Pashtu numerals recognition

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