USER-INDEPENDENT ONLINE HANDWRITTEN DIGIT RECOGNITION
This paper proposes a fast user-independent method for handwritten digit recognition. The local feature of inputting strokes is firstly coded according to the eight equiangular encircled directions. Inputting digit is then modeled with a set of rules defined with the code of local features to characterize the drawing style of inputting digit. The decision tree learning is also invoked to model the variance of drawing styles and guarantees high recognition rate. Main advantage of proposed method is twofold. Firstly, it is quite simple and highly discriminating, and can do recognition quickly under strict resource constraints. Secondly, it is insensitive to different users and guarantees user adaptability. Experiments prove our method both effective and efficient for online handwriting digit recognition.
Online Handwriting Digit Recognition User Adaptation Direction Code Decision Tree ID3
WEN-LI JIANG ZHENG-XING SUN BO YUAN WEN-TAO ZHENG WEN-HUI XU
State Key Lab for Novel Software Technology, Nanjing University, 210093, China
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
3359-3364
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）