E.A. Bobyleva - Post-graduate Student, «Computer systems and networks», Kaluga branch of the Bauman MSTU E-mail: foxymoon@yandex.ru V.E. Drach - Ph. D. (Eng.), Associate Professor, Department «Design and manufacturing of electronic equipment», Kaluga branch of the Bauman MSTU E-mail: drach@bmstu-kaluga.ru A.V. Rodionov - Ph. D. (Eng.), Associate Professor, Department «Computer systems and networks», Kaluga branch of the Bauman MSTU E-mail: andviro@gmail.com I.V. Chukhraev - Ph. D. (Eng.), Associate Professor, Head of Department «Computer systems and networks», Kaluga branch of the Bauman MSTU E-mail: igor.chukhraev@mail.ru

Text and character detection and classification in natural scene images is one of the perspective research tendencies. This article describes cascade LBP-classifiers application for detection and classification of the Japanese characters of syllabic alphabets in both the natural scene images and the text documents. In this research, cascade classifiers were trained using different sets of positive and negative images. The set of positive images consisted of printed and calligraphic characters. Experiments were performed in two stages. In the first stage, three sets of negative images were used. First set consisted of natural scene images, second set consisted of characters belonged to two other Japanese alphabets, third consisted of natural scene images and Japanese alphabet characters. In the second stage, sets of 10000 positive and 5000 negative samples and 20000 positive and 10000 negative samples were used. Experiments showed that different sets of negative images may affect on false positive detection and classification accuracy, but the increase of training samples number leads to the rise of classifier invariance and the decrease of false positive detection amount. Thus, the detection and false positive rates of the set of negative images consisted of natural scene images and Japanese alphabet characters and set of 20000 and 10000 positive and negative samples are 75−77% and 4−5% respectively.

 

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