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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...
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| 格式: | Printed Book |
| 出版: |
I.J. Image, Graphics and Signal Processing,
2013
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| 主题: | |
| 在线阅读: | 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 | ||