A Hierarchical Classification Structure based on Trainable Bayesian Classifier for Logo Detection and Recognition

The ever-increasing number of logo (trademark) in official automation systems for information management, archiving and retrieval applications has created greater demand for an automatic detection and recognition logo. In this paper, a hierarchical classification structure based on Bayesian classifier is proposed to logo detection and recognition. In this hierarchical structure, using two measures false accept rate (FAR) and false reject rate (FRR), a novel and straightforward training scheme is presented to extract optimum parameters of the trained Bayesian classifier. In each level of the hierarchical structure, a separable feature set of shape and texture features is used to train and test classifier based on complexity of the logo pattern. The logo candidate regions are extracted from document images by a wavelet-based segmentation algorithm, and then recognized in the proposed structure. The proposed structure is evaluated on a vast database consisting of the document and non-document images with Persian and international logos. The obtained results show efficiency of the proposed structure in the real and operational conditions.