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The main focus of this paper is document image classification and retrieval,where we analyze and compare different parameters for the RunLeght Histogram(RL) and Fisher Vector (FV) based image representations. We do an exhaustiveexperimental study using different document image datasets, including the MARGbenchmarks, two datasets built on customer data and the images from the PatentImage Classification task of the Clef-IP 2011. The aim of the study is to giveguidelines on how to best choose the parameters such that the same featuresperform well on different tasks. As an example of such need, we describe theImage-based Patent Retrieval task's of Clef-IP 2011, where we used the sameimage representation to predict the image type and retrieve relevant patents.

Document image classification, with a specific view on applications of patent images