Munish Kumar

Work place: Department of Computer Science, Punjab University Rural Centre, Kauni, Muktsar, Punjab



Research Interests: Speech Recognition, Pattern Recognition


Munish Kumar received his Bachelors degree in Information Technology from Punjab Technical University, Jalandhar, India in 2006 and Post Graduate degree in Computer Science & Engineering from Thapar University, Patiala, India in 2008. He received his Ph.D. degree in Computer Science from Thapar University, Patiala, Punjab, India. He started his carrier as Assistant Professor in computer application at Jaito centre of Punjabi university, Patiala. He is working as Assistant Professor in Panjab University Rural Centre, Kauni, Muktsar, Punjab, INDIA. His research interests include Character Recognition.

Author Articles
Clustering of Multi Scripts Isolated Characters Using k-Means Algorithm

By Neeru Garg Munish Kumar

DOI:, Pub. Date: 8 Aug. 2015

The aim of this paper is script identification problem of handwritten text which facilitates the clustering of data according to their type of script. In this paper, collection of different types of handwritten text document i.e. Devanagari, Gurumukhi and Roman is taken as input and then cluster of all these documents according to script type whether i.e. Devanagari, Gurumukhi, or Roman was prepared. Clustering of handwritten multi-script document scheme proposed in this paper is divided into two phases. First phase used to extract the features of given text images. In the second phase, features extracted in the previous phase were used for clustering with k-Means algorithm. In feature extraction phase, we have extracted four types of features, namely, circular curvature feature, horizontal stroke density feature, pixel density feature value and zoning based feature. In this study, we have considered 4,850 samples of isolated characters of Devanagari, Gurumukhi and Roman script.

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Segmentation of Isolated and Touching Characters in Offline Handwritten Gurmukhi Script Recognition

By Munish Kumar M. K. Jindal R. K. Sharma

DOI:, Pub. Date: 8 Jan. 2014

Segmentation of a word into characters is one of the important challenges in optical character recognition. This is even more challenging when we segment characters in an offline handwritten document. Touching characters make this problem more complex. In this paper, we have applied water reservoir based technique for identification and segmentation of touching characters in handwritten Gurmukhi words. Touching characters are segmented based on reservoir base area points. We could achieve 93.51% accuracy for character segmentation with this method. If the characters are neither broken nor overlapping, then this technique shall produce even better results.

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