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Conference Paper
Sign Recognition with HMM/SVM hybrid for the visually-handicapped in subway stations
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- Authors
- Dong-jin Lee, Ho-sub Yoon
- Issue Date
- 2012-10
- Citation
- International Conference on Neural Computation Theory and Applications (NCTA) 2012, pp.631-634
- Language
- English
- Type
- Conference Paper
- 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.
- KSP Keywords
- AdaBoost Algorithm, Classification system, False Positive(FP), False Positive Rate, Hidden markov model(HMM), Recognition Rate, South Korea, Visually handicapped, feature extraction algorithm, natural scene images, sign recognition