This paper describes an optical recognition system for the handprinted Chinese characters. It is based on a novel string representation method and an inductive learning scheme that allows flexible (or elastic) representation and matching of unknown character instances. The system scans a character instance from four different views to obtain its peripheral segment information. A string representation is designed for representing the peripheral information at each of the four views. This representation can be generalized to represent the variations in different instances of a character by using an inductive learning algorithm. A clustering algorithm is developed to group the learned representation of characters into clusters in a hierarchical tree structure. Finally, a two-stage recognition process based on the developed representation is described. Experimental results demonstrate that high recognition rates can be obtained using the developed method.
ASJC Scopus subject areas
- Control and Systems Engineering
- Theoretical Computer Science
- Computer Science Applications
- Information Systems and Management
- Artificial Intelligence