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Evaluation and Synthesis of Feature Vectors for Handwritten Numeral Recognition
Fumitaka KIMURA Shuji NISHIKAWA Tetsushi WAKABAYASHI Yasuji MIYAKE Toshio TSUTSUMIDA
IEICE TRANSACTIONS on Information and Systems
Publication Date: 1996/05/25
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Issue on Character Recognition and Document Understanding)
Category: Comparative Study
handwritten numeral recognition, feature extraction, feature selection, ZIP code recognition,
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This paper consists of two parts. The first part is devoted to comparative study on handwritten ZIP code numeral recognition using seventeen typical feature vectors and seven statistical classifiers. This part is the counterpart of the sister paper Handwritten Postal Code Recognition by Neural Network - A Comparative Study" in this special issue. In the second part, a procedure for feature synthesis from the original feature vectors is studied. In order to reduce the dimensionality of the synthesized feature vector, the effect of the dimension reduction on classification accuracy is examined. The best synthesized feature vector of size 400 achieves remarkably higher recognition accuracy than any of the original feature vectors in recognition experiment using a large number of numeral samples collected from real postal ZIP codes.