Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0031-3203(01)00237-0
Title: Combination of multiple classifiers using probabilistic dictionary and its application to postcode recognition
Authors: Lu, Y. 
Tan, C.L. 
Keywords: Combination of multiple classifiers
Numeral recognition
Postcode level
Postcode recognition
Probabilistic dictionary
Single character level
Issue Date: 2002
Source: Lu, Y., Tan, C.L. (2002). Combination of multiple classifiers using probabilistic dictionary and its application to postcode recognition. Pattern Recognition 35 (12) : 2823-2832. ScholarBank@NUS Repository. https://doi.org/10.1016/S0031-3203(01)00237-0
Abstract: Combination of multiple classifiers is regarded as an effective strategy for achieving a practical system of handwritten character recognition. A great deal of research on the methods of combining multiple classifiers has been reported to improve the recognition performance of single characters. However, in a practical application, the recognition performance of a group of characters (such as a postcode or a word) is more significant and more crucial. With the motivation of optimizing the recognition performance of postcode rather than that of single characters, this paper presents an approach to combine multiple classifiers in such a way that the combination decision is carried out at the postcode level rather than at the single character level, in which a probabilistic postcode dictionary is utilized as well to improve the postcode recognition ability. It can be seen from the experimental results that the proposed approach markedly improves the postcode recognition performance and outperforms the commonly used methods of combining multiple classifiers at the single character level. Furthermore, the sorting performance of some particular bins with respect to the postcodes with low frequency of occurrence can be improved significantly at the same time. © 2002 Pattern Recognition Society, Published by Elsevier Science Ltd. All rights reserved.
Source Title: Pattern Recognition
URI: http://scholarbank.nus.edu.sg/handle/10635/38885
ISSN: 00313203
DOI: 10.1016/S0031-3203(01)00237-0
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