Serial classifier combination for handwritten word recognition
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The performance of off-line handwritten word recognition algorithms declines with increasing lexicon size, but may be improved by serial combination of classifiers. The authors address some issues relevant to the design of serial classifier combinations. They present experimental results that show that the performance of a serial combination depends on not only the intrinsic recognition power of the classifiers but also the relative orthogonality of their features. A top-choice recognition rate of 83% is obtained for a lexicon of size 1700 by combining two analytical word classifiers that perform individually at 70%. Even higher recognition rates may be expected from a serial combination of two classifiers with less correlated features, such as a high-performance holistic classifier with an analytical classifier.
[1] James M. Keller,et al. Information fusion in computer vision using the fuzzy integral , 1990, IEEE Trans. Syst. Man Cybern..
[2] Adam Krzyżak,et al. Methods of combining multiple classifiers and their applications to handwriting recognition , 1992, IEEE Trans. Syst. Man Cybern..
[3] J.-C. Simon,et al. Formes régulières et singulières; application à la reconnaissance de l'écriture manuscrite , 1989 .
[4] Sargur N. Srihari,et al. Decision Combination in Multiple Classifier Systems , 1994, IEEE Trans. Pattern Anal. Mach. Intell..