IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal sampling intervals for Gabor features and printed Japanese character recognition
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Gabor Feature Extraction for Character Recognition: Comparison with Gradient Feature
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
A Bilingual Machine-Interface OCR for Printed Kannada and English Text Employing Wavelet Features
ICIT '07 Proceedings of the 10th International Conference on Information Technology
ICDIP '09 Proceedings of the International Conference on Digital Image Processing
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Handwriting character recognition (HCR) for Indian Languages is an important problem where there is relatively little work has been done. In this paper, we investigate the moments features on Kannada handwritten basic character set of 49 letters. Moments features are extracted from the preprocessed original images by most of the researchers. Kannada characters are curved in nature with some symmetry observed in the shape. This information can be best extracted as a feature if we extract moment features from the directional images. So we are finding 4 directional images using Gabor wavelets from the dynamically preprocessed original images. We then extract moments features from them. The comparison of moments features of 4 directional images with original images when tested on Multi Layer Perceptron with Back Propagation Neural Network shows an average improvement of 13% from 72% to 85%. The mean performance of the system with these two features together is 92%.