Title :
A branch and bound decision tree Bayes classifier for robust multi-font printed Chinese character recognition
Author :
Chan, Chorkin ; Wong, Pak-bong
Author_Institution :
Dept. of Comput. Sci., Hong Kong Univ., Hong Kong
Abstract :
A branch and bound classifier is proposed as an m-ary decision tree with each node representing a set of disjoint classes. Associated with each set is a space S and an estimate of the maximum likelihood of any x in S belonging to a class of the set. By comparing this estimate with the best-likelihood-found-so-far for x, it can be decided if the node is worth visiting. This classifier is applied to recognize 4879 classes of multi-font printed Chinese characters with practically the same recognition rate (98%), but in 5% of the time required, when compared with a full-search Bayes classifier
Keywords :
Bayes methods; character recognition; decision theory; branch and bound decision tree Bayes classifier; m-ary decision tree; maximum likelihood; robust multifont printed Chinese character recognition; Character recognition; Classification tree analysis; Computer science; Decision trees; Error analysis; Partitioning algorithms; Pattern matching; Robustness; Yield estimation;
Conference_Titel :
TENCON '92. ''Technology Enabling Tomorrow : Computers, Communications and Automation towards the 21st Century.' 1992 IEEE Region 10 International Conference.
Conference_Location :
Melbourne, Vic.
Print_ISBN :
0-7803-0849-2
DOI :
10.1109/TENCON.1992.271942
