On the possibility of structure learning-based scene character detector

Yugo Terada, Rong Huang, Yaokai Feng, Seiichi Uchida

Research output: Contribution to journalConference article

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number6628666
Pages (from-to)472-476
Number of pages5
JournalProceedings of the International Conference on Document Analysis and Recognition, ICDAR
DOIs
Publication statusPublished - 2013
Event12th International Conference on Document Analysis and Recognition, ICDAR 2013 - Washington, DC, United States
Duration: Aug 25 2013Aug 28 2013

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

Fingerprint Dive into the research topics of 'On the possibility of structure learning-based scene character detector'. Together they form a unique fingerprint.

  • Cite this