Abstract
In this research we demonstrate the improvement for handwritten recognition using edge detection technique and our novel technique of adding intensive data. We collect totally 600 signatures from 30 people. Then we transform the hand written signatures images to be image file and resize to 144 x 38 pixels along the width and the height, respectively. Every pixel is encoded its intensity value from 0 to 255. The value 0 is the highest intensity (black) and 255 is white. Next, we use 4 different algorithms: Support Vector Machine (with linear, polynomial, radial basis, and sigmoid kernel functions), k-Nearest Neighbors, Perceptron, and Naive Bayes (using Gaussian, multinomial, and Bernoulli density functions). From the experiment result, SVM with polynomial kernel function shows the highest accuracy (95.33%). Then we use 4 techniques of edge detection:Sobel, Prewitt, Robert, Canny and Thinning technique. WithSobel edge detection technique, we found that the accuracy is gained to 96% (higher than the highest of original data). We also observe that Sobel technique can improve the accuracy of k-NN with a significant level (from 78.67% to 91.33%). Moreover, we try to append the high intensity color data. And by this technique, we notice significant improvement of k-NN accuracy up to 96%. In SVM with linear function, after applying our technique the accuracy is improved to 98.00% which is the highest accuracy of this research.
Highlights
The use of biometric in authentication or individual identification receives much attention in the current
This paper proposes a technique to enhance the signature recognition by focusing the improvement of signature images.The signature images will be improved by edge detection technique and thinning edgetechnique
The four algorithms used in this study include Perceptron algorithm, Support Vector Machine algorithm
Summary
The use of biometric in authentication or individual identification receives much attention in the current. It provides convenience of not having to carry identification documents, which reduces the problem of document falsification. The signature is external identity which is widely used for identifying individual. Signature of a person is distinct and it is hardly to be forged or counterfeited. Vargas et al(1)reviewed the handwritten signatures focusing on the grey-scale measurement and co-occurrence matrix technique and local binary pattern base on MCYT-75 and GPDS-100 databases. The result was that the EER (Equal Error Rate) = 16.27%
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