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On combining classifiers
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1998
"... We develop a common theoretical framework for combining classifiers which use distinct pattern representations and show that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision. An experimental ..."
Abstract

Cited by 1392 (32 self)
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We develop a common theoretical framework for combining classifiers which use distinct pattern representations and show that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision. An experimental comparison of various classifier combination schemes demonstrates that the combination rule developed under the most restrictive assumptionsâ€”the sum ruleâ€”outperforms other classifier combinations schemes. A sensitivity analysis of the various schemes to estimation errors is carried out to show that this finding can be justified theoretically.
The recognition of handwritten digit strings of unknown length using hidden Markov models
 In Proc. of 14 th International Conference Pattern Recognition (ICPR
, 1998
"... We apply an HMMbased text recognition system to the recognition of handwritten digit strings of unknown length. The algorithm is tailored to the input data by controlling the maximum number of levels searched by the Level Building (LB) search algorithm. We demonstrate that setting this parameter ac ..."
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Cited by 6 (0 self)
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We apply an HMMbased text recognition system to the recognition of handwritten digit strings of unknown length. The algorithm is tailored to the input data by controlling the maximum number of levels searched by the Level Building (LB) search algorithm. We demonstrate that setting this parameter according to the pixel length of the observation sequence, rather than using a fixed value for all input data, results in a faster and more accurate system. Best results were achieved by setting the maximum number of levels to twice the estimated number of characters in the input string. We also describe experiments which show the potential for further improvement by using an adaptive termination criterion in the LB search. 1. Introduction Hidden Markov models (HMMs) [5] have been widely used in the field of speech recognition for many years [4], but have only recently begun to receive a similar degree of attention in the context of text recognition [1, 3]. The HMM approach is particularly su...
A method for connected handprinted numeral recognition using hidden Markov models
"... A method for the recognition of handprinted numerals using hidden Markov models is described. The method involves the representation of 2D images of a character with two 1D models, one for the pixel columns of the image and the other for the rows. Various normalisations are applied to both the trai ..."
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A method for the recognition of handprinted numerals using hidden Markov models is described. The method involves the representation of 2D images of a character with two 1D models, one for the pixel columns of the image and the other for the rows. Various normalisations are applied to both the training and test data to reduce variations between characters within a class, resulting in a corresponding improvement in classification performance. In our latest experiments, a character recognition rate of over 93% was achieved on digit strings of variable length. 1 Introduction The authors have previously described the use of hidden Markov models (HMMs) for the recognition of noisy printed text [814] and have demonstrated how HMMs can be used to jointly segment and classify strings of connected characters which occur in, for example, faxed documents [15]. The same approach can be used for the recognition of strings of potentially connected handprinted numerals such as ZIP codes. These n...