Sequential Operations in Digital Picture Processing
Journal of the ACM (JACM)
Gujarati Character Recognition
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Sparse Texture Representation Using Local Affine Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
A statistical framework for genomic data fusion
Bioinformatics
Zone Identification in the Printed Gujarati Text
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
A Visual Vocabulary for Flower Classification
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
More efficiency in multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Wavelet Feature Based Confusion Character Sets for Gujarati Script
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 02
Shape Descriptor Based Document Image Indexing and Symbol Recognition
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
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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.