1 March 2005 Character recognition by best anisotropic local bases and neural networks
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The best basis paradigm is a lower cost alternative to the principal component analysis (PCA) for feature extraction in pattern recognition applications. Its main idea is to build a collection of bases and search for the best one in terms of e.g. best class separation. Recently, fast best basis search algorithms have been generalized for anisotropic wavelet packet bases. Anisotropy is preferable for 2-D objects since it helps capturing local image features in a better way. In this contribution, the best anisotropic basis search framework is applied to the problem of recognition of characters captured from gray-scale pictures of car license plates. The goals are to simplify the classifier and to avoid a preliminary binarization stage by extracting features directly from the gray-scale images. The collection of bases is formed by anisotropic wavelet packets. The search algorithm seeks for a basis providing the lowest-dimensional data representation preserving the inter-class separability for given training data set, measured as Euclidean distance between class centroids. The relationship between the feature extractor and classifier complexity is clarified by training neural networks for different local bases. The proposed methodology shows its superiority to PCA as it yields equal and even lower classification error rate with considerably reduced computational costs.
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Vladislav I. Uzunov, Atanas P. Gotchev, Heikki J. Huttunen, Karen O. Egiazarian, Jaakko T. Astola, "Character recognition by best anisotropic local bases and neural networks", Proc. SPIE 5672, Image Processing: Algorithms and Systems IV, (1 March 2005); doi: 10.1117/12.587543; https://doi.org/10.1117/12.587543

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