Authors:
Dong-jin Lee
1
and
Ho-sub Yoon
2
Affiliations:
1
University of Science and Technology, Korea, Republic of
;
2
Electronics and Telecommunications Research Institute, Korea, Republic of
Keyword(s):
Sign Recognition, Character Recognition, Hybrid HMM/SVM, Feature Extraction, Natural Scene Images.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
In this paper, we propose a sign classification system to recognize exit number and arrow signs in natural scene images. The purpose of the sign classification system is to provide assistance to a visually-handicapped person in subway stations. For automatically extracting sign candidate regions, we use Adaboost algorithm, however, our detector not only extracts sign regions, but also non-sign (noise) regions in natural scene images. Thus, we suggest a verification technique to discriminate sign regions from non-sign regions. In addition, we suggest a novel feature extraction algorithm cooperated with Hidden Markov Model. To evaluate the system, we tested a total of 20,177 sign candidate regions including the number of 8,414 non-sign regions on the captured images under several real environments in Daejeon in South Korea. We achieved an exit number and arrow sign recognition rate of each 99.5% and 99.8% and a false positive rate (FPR) of 0.3% to discriminate between sign regions and
non-sign regions.
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