A keypoint-based approach toward scenery character detection

Seiichi Uchida, Yuki Shigeyoshi, Yasuhiro Kunishige, Feng Yaokai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

22 Citations (Scopus)

Abstract

This paper proposes a new approach toward scenery character detection. This is a key point-based approach where local features and a saliency map are fully utilized. Local features, such as SIFT and SURF, have been commonly used for computer vision and object pattern recognition problems, however, they have been rarely employed in character recognition and detection problems. Local feature, however, is similar to directional features, which have been employed in character recognition applications. In addition, local feature can detect corners and thus it is suitable for detecting characters, which are generally comprised of many corners. For evaluating the performance of the local feature, an experimental result was done and its results showed that SURF, i.e., a simple gradient feature, can detect about 70% of characters in scenery images. Then the saliency map was employed as an additional feature to the local feature. This trial is based on the expectation that scenery characters are generally printed to be salient and thus higher salient area will have a higher probability to be a character area. An experimental result showed that this expectation was reasonable and we can have better discrimination accuracy with the saliency map.

Original languageEnglish
Title of host publicationProceedings - 11th International Conference on Document Analysis and Recognition, ICDAR 2011
Pages819-823
Number of pages5
DOIs
Publication statusPublished - 2011
Event11th International Conference on Document Analysis and Recognition, ICDAR 2011 - Beijing, China
Duration: Sep 18 2011Sep 21 2011

Publication series

NameProceedings of the International Conference on Document Analysis and Recognition, ICDAR
ISSN (Print)1520-5363

Other

Other11th International Conference on Document Analysis and Recognition, ICDAR 2011
CountryChina
CityBeijing
Period9/18/119/21/11

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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