A Hierarchical Logo Detection and Recognition Algorithm Using Two-Stage Segmentation and Multiple Classifiers

Logo detection and recognition module is a principle requirement in official automation systems for document image archiving and retrieval applications. In this paper, we present a logo detection and recognition algorithm based on sequential segmentation and classification strategy of document image. In this framework, using a two-stage segmentation algorithm (consisting of wavelet-based and threshold-based segmentation algorithm) and hierarchical classification by two multilayer perceptron (MLP) classifiers and a k-nearest neighbor (KNN) classifier, a document image divides to text, pure picture and logo candidate regions. Ultimately, in final decision, class of logo candidate region is determined based on pre-defined classes. In the hierarchical classification and logo recognition stages, the best feature space is selected by forward selection algorithm from a perfect set of texture and shape features. The proposed structure is evaluated on a variety and vast database consisting of the document and non-document images with Persian and international logos. The obtained results show efficiency of the proposed framework in the real and operational conditions.