@inproceedings{oai:soar-ir.repo.nii.ac.jp:00013243,
 author = {MARUYAMA,  Minoru and YAMAGUCHI,  Takuma},
 month = {},
 note = {We present a method to detect characters on signboardsin natural scene images. For many applications, both classifierwith small computational cost and the efficient featureset, which gives rise to accurate recognition are required.Texture based features are often used for target detection. Ithas been also shown that the shape of the intensity distributionis often useful for character extraction. The intensitydistribution in the character regions is often different fromthe unimodal distribution. We measure the discrepancy betweenthe observed region and the normal distribution byskewness and kurtosis. We use these statistics along withthe texture based features. Character regions in a naturalscene image are detected by using the linear combination ofstump classifiers, each of which sees only one component ofmultidimensional feature vector. Selection of a feature componentfor each stump and determination of coefficients oflinear combination are carried out by AdaBoost. We experimentallyshow the effectiveness of the proposed method., Article, ICDAR2009: 1365-1369 (2009)},
 pages = {1365--1369},
 publisher = {International Conference on Document Analysis and Recognition},
 title = {Extraction of characters on signboards in natural scene images by stump classifiers Proc.10th International Conference on Document Analysis and Recognition},
 year = {2009}
}