Classification of printed Gujarati characters using som based k-Nearest Neighbor Classifier

This paper presents a method for combining Self Organizing Map (SOM) with k-Nearest Neighbor Classifier (k-NN) to device an elegant classification technique and applying it for classification of subset of printed Gujarati characters. Many researchers have employed many different models for the class...

Full description

Saved in:
Bibliographic Details
Main Authors: Goswami, M. M., Prajapati, H. B., Dabhi, V. K.
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Character recognition
Classification algorithms
Gujarati Character Classification
Information processing
Optical character recognition software
Printed Character Classification
Self-Organizing Maps
Support vector machines
Training
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents a method for combining Self Organizing Map (SOM) with k-Nearest Neighbor Classifier (k-NN) to device an elegant classification technique and applying it for classification of subset of printed Gujarati characters. Many researchers have employed many different models for the classification of printed/handwritten characters for number of different languages all over the globe; few of the widely used classifiers are Template Matching, Artificial Neural Network (ANN), Hidden Markov Model (HMM), and Support Vector Machine (SVM) etc. Our attempt is to use SOM based k-NN classifier for classification of subset of printed Gujarati characters. This approach does not require prior feature identification stage hence it is faster and more generalize compare to other approaches. A prototype system is implemented for the same and tested on sufficient dataset. Average accuracy of 82.36% is reported on test dataset.
DOI:10.1109/ICIIP.2011.6108882