Abstract

This paper proposes an automated system for rotation and scale invariant logo recognition system based on black and white logo images. Logo images are recognized using two shape features namely Moment Invariants and Hough Transform. For Moment Invariant Method the first two central normalized moments out of Hu-s seven invariant moments are used In case of Hough Transform, first Standard Hough Transform (SHT) is performed. The Hough Transform matrix (H) along with array of theta and rho values over which H is generated is computed. Then six large singular values are calculated from this three parameters and they are added together to form the specified Hough Transform Feature. The data set consists of about 1700 black and white logo images where there are 100 different classes in which each class has got rotation, scaling and composite variations of each image, which are classified using Manhattan and Euclidian Distances. The user also has the flexibility of applying any arbitrary angle of rotation and scaling factor over the logo image and then correctly recognizing the logo, thus making this approach a rotation and scale invariant one. The proposed approach is highly scalable and robust providing better accuracy results than other techniques.

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