Font distribution observation by network-based analysis

Chihiro Nakamoto, Rong Huang, Sota Koizumi, Ryosuke Ishida, Yaokai Feng, Seiichi Uchida

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

1 被引用数 (Scopus)


The off-the-shelf Optical Character Recognition (OCR) engines return mediocre performance on the decorative characters which usually appear in natural scenes such as signboards. A reasonable way towards the so-called camera-based OCR is to collect a large-scale font set and analyze the distribution of font samples for realizing some character recognition engine which is tolerant to font shape variations. This paper is concerned with the issue of font distribution analysis by network. Minimum Spanning Tree (MST) is employed to construct font network with respect to Chamfer distance. After clustering, some centrality criterion, namely closeness centrality, eccentricity centrality or betweenness centrality, is introduced for extracting typical font samples. The network structure allows us to observe the font shape transition between any two samples, which is useful to create new fonts and recognize unseen decorative characters. Moreover, unlike the Principal Component Analysis (PCA), the font network fulfills distribution visualization through measuring the dissimilarity between samples rather than the lossy processing of dimensionality reduction. Compared with K-means algorithm, network-based clustering has the ability to preserve small size font clusters which generally consist of samples taking special appearances. Experiments demonstrate that the proposed network-based analysis is an effective way to grasp font distribution, and thus provides helpful information for decorative character recognition.

ホスト出版物のタイトルCamera-Based Document Analysis and Recognition - 5th International Workshop, CBDAR 2013, Revised Selected Papers
出版社Springer Verlag
出版ステータス出版済み - 1 1 2014
イベント5th International Workshop on Camera-Based Document Analysis and Recognition, CBDAR 2013 - Washington, DC, 米国
継続期間: 8 23 20138 23 2013


名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
8357 LNCS


その他5th International Workshop on Camera-Based Document Analysis and Recognition, CBDAR 2013
CityWashington, DC

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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