Evaluation and Synthesis of Feature Vectors for Handwritten Numeral Recognition (Special Issue on Character Recognition and Document Understanding)

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概要

• 論文の詳細を見る

• 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.

• 社団法人電子情報通信学会の論文
• 1996-05-25

著者

• Wakabayashi Tetsushi

  Graduate School of Engineering, Mie University

• Kimura Fumitaka

  Faculty Of Engineering Mie University

• Wakabayashi Tetsushi

  Graduate School Of Engineering Mie University

• Wakabayashi Tetsushi

  Faculty Of Engineering Mie University

• NISHIKAWA Shuji

  Faculty of Engineering, Mie University

• MIYAKE Yasuji

  Faculty of Engineering, Mie University

• TSUTSUMIDA Toshio

  Technology Department Research Center, Institute for Posts and Telecommunications Policy, Ministry o

• Kimura F

  Faculty Of Engineering Mie University

• Miyake Y

  Faculty Of Engineering Mie University

• Miyake Yasuji

  Faculty Of Engineering Mie University

• Tsutsumida Toshio

  Technology Department Research Center Institute For Posts And Telecommunications Policy Ministry Of

• Nishikawa Shuji

  Faculty Of Engineering Mie University:nec Corporation

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