Off-line Recognition of Unconstrained Handwritten Character using Decision Tree

초록

This paper addresses a recognition technique for unconstrained handwritten characters based on a novel feature extraction and decision tree classifier. ESP(Extended Stroke Primitive) is defined as a part of stroke, a stroke itself, or consecutive multiple strokes. Input character image is thinined using a parallel thining algorithm, and feature nodes are extracted and characterized from the thined image. ESPs are extracted from the feature nodes, and they are represented as feature vectors. Feature vectors are classified using a decision tree classifier. The decision tree is constructed based on ID3 learning algorithm. We have achieved very encouraging experimental results.