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http://dx.doi.org/10.9708/jksci.2011.16.9.027

Online Handwritten Digit Recognition by Smith-Waterman Alignment  

Mun, Won-Ho (Dept. of Computer Engineering, Pusan National University)
Choi, Yeon-Seok (Dept. of Computer Engineering, Pusan National University)
Lee, Sang-Geol (Dept. of Computer Engineering, Pusan National University)
Cha, Eui-Young (Dept. of Computer Engineering, Pusan National University)
Publication Information
Journal of the Korea Society of Computer and Information / v.16, no.9, 2011 , pp. 27-33 More about this Journal
Abstract
In this paper, we propose an efficient on-line handwritten digit recognition base on Convex-Concave curves feature which is extracted by a chain code sequence using Smith-Waterman alignment algorithm. The time sequential signal from mouse movement on the writing pad is described as a sequence of consecutive points on the x-y plane. So, we can create data-set which are successive and time-sequential pixel position data by preprocessing. Data preprocessed is used for Convex-Concave curves feature extraction. This feature is scale-, translation-, and rotation-invariant. The extracted specific feature is fed to a Smith-Waterman alignment algorithm, which in turn classifies it as one of the nine digits. In comparison with backpropagation neural network, Smith-Waterman alignment has the more outstanding performance.
Keywords
Handwritten digit recognition; Smith-Waterman alignment algorithm; Character recognition;
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