Φορτώνει......

A System for Offline Recognition of Handwritten Characters in Malayalam Script

In this paper, we propose a handwritten character recognition system for Malayalam language. The feature extraction phase consists of gradient and curvature calculation and dimensionality reduction using Principal Component Analysis. Directional information from the arc tangent of gradient is used a...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Jomy John, Kannan Balakrishnan, Pramod K. V
Μορφή: Printed Book
Έκδοση: I.J. Image, Graphics and Signal Processing, 2013
Θέματα:
Διαθέσιμο Online:http://10.26.1.76/ks/005739.pdf
LEADER 01403nam a22001457a 4500
100 |a Jomy John, Kannan Balakrishnan, Pramod K. V  |9 29980 
245 |a A System for Offline Recognition of Handwritten Characters in Malayalam Script 
260 |b I.J. Image, Graphics and Signal Processing,   |c 2013 
300 |a p.53-59  |b 4 
520 |a In this paper, we propose a handwritten character recognition system for Malayalam language. The feature extraction phase consists of gradient and curvature calculation and dimensionality reduction using Principal Component Analysis. Directional information from the arc tangent of gradient is used as gradient feature. Strength of gradient in curvature direction is used as the curvature feature. The proposed system uses a combination of gradient and curvature feature in reduced dimension as the feature vector. For classification, discriminative power of Support Vector Machine (SVM) is evaluated. The results reveal that SVM with Radial Basis Function (RBF) kernel yield the best performance with 96.28% and 97.96% of accuracy in two different datasets. This is the highest accuracy ever reported on these datasets. 
650 |a CHARACTER RECOGNITION;  |a GRADIENT;  |a CURVATURE;  |a PRINCIPAL COMPONENT ANALYSIS  |9 29981 
856 |u http://10.26.1.76/ks/005739.pdf 
942 |c KS 
999 |c 81238  |d 81238 
952 |0 0  |1 0  |4 0  |7 0  |9 73263  |a MGUL  |b MGUL  |d 2016-04-02  |l 0  |r 2016-04-02  |w 2016-04-02  |y KS