Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
A Multichannel Approach to Fingerprint Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Access Structures for Angular Similarity Queries
IEEE Transactions on Knowledge and Data Engineering
Journal of Cognitive Neuroscience
A System for Off-Line Oriya Handwritten Character Recognition Using Curvature Feature
ICIT '07 Proceedings of the 10th International Conference on Information Technology
Handwritten character recognition using elastic matching and PCA
Proceedings of the International Conference on Advances in Computing, Communication and Control
FLD Based Unconstrained Handwritten Kannada Character Recognition
FGCNS '08 Proceedings of the 2008 Second International Conference on Future Generation Communication and Networking Symposia - Volume 03
Recognition of handwritten Chinese characters by critical region analysis
Pattern Recognition
The use of radon transform in handwritten Arabic (Indian) numerals recognition
WSEAS Transactions on Computers
Local descriptors in application to the aging problem in face recognition
Pattern Recognition
| Hi-index | 0.00 |
In this paper, we compare the affect of four different similarity measure techniques namely Euclidean distance, Modified squared euclidean distance, Correlation distance and Angle distance for an unconstrained handwritten character recognition. The strength of these similarity measures are estimated between feature vectors with respect to the recognition performance of the Gabor-PCA method. Gabor filter is used to extract spatially localized features of character image. The dimensions of such Gabor feature vector is prohibitively high & in order to compress Gabor features we used PCA method. The experiments were performed using the database containing 22,600 samples of Kannada and English. From the analysis the better recognition accuracy were achieved using angle distance measure.