Chinese Character Recognition Method Research Articles

On-line Chinese character recognition using ART-based stroke classification

This paper proposes an on-line Chinese character recognition method using Adaptive Resonance Theory (ART) based stroke classification. Strokes, primitive components of Chinese 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 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 stroke classifier contributes to high stroke recognition rate and less recognition time. A database for character recognition also dynamically constructed with generalized character lists, and a new character can be included simply by adding a new sequence to the list. Character recognition is achieved by traversing the Chinese character database with a sequence of recognized strokes and positional relations between the strokes. To verify the performance of the system, we tested it on 1800 basic Chinese characters used daily in Korea, and obtained a good recognition rate of 93.13%. These results suggest that the proposed system is pertinent to be put into practical use.

Feature point method of Chinese character recognition and its application

A new method for recognizing Chinese characters is proposed. It is based on the so- called feature points of Chinese characters. The feature points we use include those on the stroke of a character, i.e., end points, turning points, fork points and cross points, and the key points on the background of character. This method differs from the previous ones for it combines the feature points on stroke with those on back-ground and it uses feature points to recognize Chinese characters directly. A Chinese character recognition system based on top- down dynamical matching of feature point is developed. The system can recognize not only 6763 printed sample Song font Chinese characters of size 5.6×5.6mm2 with high recognition rate, but also the general printed books, magazines and documents with a satisfactory recognition rate and speed.

A knowledge-based stroke-matching method for Chinese character recognition

The character set used by the Chinese is greater in both size and complexity than that used by the Japanese, and experiments performed by the authors indicate that the stroke-matching algorithm of Yamamoto and Rosenfeld (1982) is too computationally intensive to be implemented for the recognition of complicated Chinese characters. A modified relaxation method that greatly reduces the computational load is proposed. The idea is to incorporate specific knowledge about the Chinese characters into the training system to remove inconsistencies with a higher efficiency and to speed up the matching process. Experimental results on the 240 most frequently used characters written by 37 different individuals showed that a fourfold improvement in speed could be achieved, making it more feasible to implement the algorithm for the recognition of complicated Chinese characters.