On the possibility of structure learning-based scene character detector

Yugo Terada, Rong Huang, Yaokai Feng, Seiichi Uchida

研究成果: ジャーナルへの寄稿Conference article

4 引用 (Scopus)

抄録

In this paper, we propose a structure learning-based scene character detector which is inspired by the observation that characters have their own inherent structures compared with the background. Graphs are extracted from the thinned binary image to represent the topological line structures of scene contents. Then, a graph classifier, namely gBoost classifier, is trained with the intent to seek out the inherent structures of character and the counterparts of non-character. The experimental results show that the proposed detector achieves the remarkable classification performance with the accuracy of about 70%, which demonstrates the existence and separability of the inherent structures.

元の言語英語
記事番号6628666
ページ(範囲)472-476
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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Classifiers
Detectors
Binary images

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

これを引用

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