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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Handwritten Digits Recognition Improved by Multiresolution Classifier Fusion
verfasst von : Miroslav Štrba, Adam Herout, Jiří Havel
Erschienen in: Pattern Recognition and Image Analysis
Verlag: Springer Berlin Heidelberg
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One common approach to construction of highly accurate classifiers for hadwritten digit recognition is fusion of several weaker classifiers into a compound one, which (when meeting some constraints) outperforms all the individual fused classifiers. This paper studies the possibility of fusing classifiers of different kinds (Self-Organizing Maps, Randomized Trees, and AdaBoost with MB-LBP weak hypotheses) constructed on training sets resampled to different resolutions. While it is common to select one resolution of the input samples as the “ideal one” and fuse classifiers constructed for it, this paper shows that the accuracy of classification can be improved by fusing information from several scales.