Urdu Handwritten Words Recognition Using Machine Learning

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Adnan Hussain Shah
Muhammad Majid Mahmood Bagram
Muhammad Munwar Iqbal
Farooq Ali

Abstract

The recognition of cursive or a running hand script is considered a difficult task in character recognition because it has a different representation style. Urdu originated from Arabic script, which is why it is much closer to Arabic script, has similar challenges and complications but with more intensity level. There are different styles of writing Urdu, but commonly Urdu alphabet is written in Nastalik script. This research work done on Convolutional Neural Networks with Mobile Net architecture with a Machine learning technique, i.e. Transfer Learning, makes it much easier. It is a technique where the model is developed for one task and then re-used as a starting point for different tasks. There are 603 images of Urdu Handwritten Words with 44 classes written by a different writer in datasets. The size of all images is 64*64, which is trained through the transfer learning technique. Code written in python using different python libraries like Keras, Ski learn, Numpy, etc., and accuracy is 90%, which is very efficient, later discussed in the last sections.

Article Details

Section
COMPUTER SCIENCE
Author Biography

Muhammad Majid Mahmood Bagram, Department of Business Administration, Allama Iqbal Open University, Islamabad, Pakistan

Dr. Muhammad Majid Mahmood Bagram has 28+ years of diversified experience with prestigious national and international organizations particularly in the area of soft skills. PhD in the discipline of Management Sciences; qualified one-year Training of Trainers Program organized jointly by the Lahore University of Management Sciences, Pakistan and McGill University, Canada; Live Morning Shows; YouTube Channel “Happiness”; and also served as Member Academic Board of the Commonwealth of Learning, Canada. He has participated in number of national and international conferences & seminars in Pakistan, India, China and United States of America. At present, he is serving as Associate Professor, Department of Business Administration, Allama Iqbal Open University, Islamabad, Pakistan.  

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