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 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.