A Hierarchical Structure of Classification based on Trainable Bayesian Classifier for Logo Detection and Recognition in Document Image

The ever-increasing number of logo (trademark) in official automation systems for information management, archiving and retrieval application has created greater demand for an automatic detection and recognition logo. In this paper, a classification hierarchical structure based on Bayesian classifier is proposed to logo detection and recognition. In this hierarchical structure, using two measures false accept and false reject, 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 candidate regions for logo 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 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 structure in the real and operational conditions.

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