Analyzing the distribution of a large-scale character pattern set using relative neighborhood graph

Masanori Goto, Ryosuke Ishida, Yaokai Feng, Seiichi Uchida

研究成果: ジャーナルへの寄稿Conference article

11 引用 (Scopus)

抄録

The goal of this research is to understand the true distribution of character patterns. Advances in computer technology for mass storage and digital processing have paved way to process a massive dataset for various pattern recognition problems. If we can represent and analyze the distribution of a large-scale character pattern set directly and understand its relationships deeply, it should be helpful for improving character recognizer. For this purpose, we propose a network analysis method to represent the distribution of patterns using a relative neighborhood graph and its clustered version. In this paper, the properties and validity of the proposed method are confirmed on 410,564 machine-printed digit patterns and 622,660 handwritten digit patterns which were manually ground-truthed and resized to 16 times 16 pixels. Our network analysis method represents the distribution of the patterns without any assumption, approximation or loss.

元の言語英語
記事番号6628575
ページ(範囲)3-7
ページ数5
ジャーナルProceedings of the International Conference on Document Analysis and Recognition, ICDAR
DOI
出版物ステータス出版済み - 12 11 2013
イベント12th International Conference on Document Analysis and Recognition, ICDAR 2013 - Washington, DC, 米国
継続期間: 8 25 20138 28 2013

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Electric network analysis
Digital signal processing
Pattern recognition
Pixels

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

これを引用

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