Use of MKL as symbol classifier for Gujarati character recognition

Abstract

The present work is part of ongoing effort to improve the performance of Gujarati character recognition. In the recent advancement in kernel methods, the novel concept of multiple kernel learning(MKL) has given improved results for many problems. In this paper, we present novel application of MKL for Gujarati character recognition. We have applied three different feature representations for symbols obtained after zone wise segmentation of Gujarati text. The MKL based classification is proposed, where the MKL is used for learning optimal combination of different features for classification. In addition MKL based classification results for different features is also presented. The multiclass classification is performed in Decision DAG framework. The comparison results in 1-Vs-1 framework and using KNN classifier is also presented. The experiments have shown substantial improvement in earlier results.

Structural Feature Based Classification of Printed Gujarati Characters

“…have done well [5][6][7][8]. Some work on classification of Gujarati text using transform domain features can be found in the literature [9][10][11][12]. Gujarati is derived from the ancient Devanagari script and having close resemblance with other north Indian script, primarily Hindi.…”