On-Line Recognition of Cursive Korean Characters Using Art-based Stroke Classification (Recognition of Cursive Korean Characters)

This paper proposes an efficient method for on-line recognition of cursive Korean characters. Strokes, primitive components of Korean characters, are usually warped into a cursive form and classifying them is very difficult. To deal with such cursive strokes, we consider them as a recognition unit and automatically classify them by using an ART-2 neural network. The neural network has the advantage of assembling similar patterns together to form classes in a self-organized manner. This ART-2 stroke classifier contributes to high stroke recognition rate and less recognition time. A database for character recognition is also dynamically constructed with a tree structure, and a new character can be included simply by adding a new sequence to it. Character recognition is achieved by traversing the database with a sequence of recognized strokes and positional relations between the strokes. To verify the performance of the system, we tested it on 17,500 handwritten characters, and obtained a good recognition rate of 96.8% and a speed of 0.52 second per character. This results suggest that the proposed method is pertinent to be put into practical use.