Fast Online Handwritten Digits Recognition Based on Decision Tree
基于决策树的快速在线手写数字识别技术

This paper proposed a fast method for handwritten digit recognition. The proposed method modeled the handwritten digits with the variation of stroke direction, and did the classification by decision tree learning. The extraction of stroke direction variation is simple, highly discriminable and insensitive to different users, implementing fast recognition under strict resource constraints, and the user adaptability is guaranteed at the same time. The decision tree learning is able to cover all the variance in user input, which guaranteed high recognition rate. The outcome of the experiments demonstrated the proposed method to be simple and effective with good user adaptability.