Abstract
During the last few years, research in recognition of handwritten Chinese and Japanese characters has matured significantly. However, in order to obtain high recognition rate, most character recognition systems have paid an extremely high computational cost. For high-performance character recognition systems, reduction of the expensive computational cost has become a very important goal. Discriminant function is a very important factor for precise pattern recognition. The Mahalanobis distance is considered as an effective function. However, precise calculation of the Mahalanobis distance requires extremely large numbers of training samples. In this article, by investigating the relationwhip of elements of feature vector, a new discriminant function, the vector-partitioned Mahalanobis distance, is proposed. With the proposed method, high recognition performance can be obtained with less computational cost. Because the proposed method partitions high dimensional feature vector into several small number dimensional vectors, the ratio of the number of training samples to the number of dimensions becomes larger. This method is especially effective in the case of lack of training samples. The effectiveness of the proposed method is shown by experimental results with the database ETL9B. © 1999 Scripta Technica, Syst Comp Jpn, 30(14): 33-42, 1999
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.