Metadata about: Logo detection methods usually depend on logo shapes and need for training data or a-priori information on the processed images. This limits their effectiveness to real-world applications. In this paper, we tackle these challenges by exploring the textural information. Specifically we propose a novel approach for administrative logo detection based on a fuzzy classification with a multi-fractal texture feature, capable of automatically characterizing texture measures describing logo and non-logo regions. Experimental results, using two real datasets, confirm the feasibility of the proposed method for degraded administrative documents. Extensive comparative evaluations demonstrate the superiority of this approach over the state-of-the-art methods.

About: Logo detection methods usually depend on logo shapes and need for training data or a-priori information on the processed images. This limits their effectiveness to real-world applications. In this paper, we tackle these challenges by exploring the textural information. Specifically we propose a novel approach for administrative logo detection based on a fuzzy classification with a multi-fractal texture feature, capable of automatically characterizing texture measures describing logo and non-logo regions. Experimental results, using two real datasets, confirm the feasibility of the proposed method for degraded administrative documents. Extensive comparative evaluations demonstrate the superiority of this approach over the state-of-the-art methods.

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