Detection and Classification of Interesting Parts in Scanned Documents by Means of AdaBoost Classification and Low-Level Features Verification

This paper presents a novel approach to detection and identification of selected document's parts stamps, logos, printed text blocks, signatures and tables on digital images obtained through paper document scanning. This task is realized in two main steps. The first one includes element detection, which is done by means of AdaBoost cascade of weak classifiers. Resulting image blocks are, in the second step, subjected to verification process. Eight feature vectors based on recently proposed descriptors were selected and combined with six different classifiers that represent numerous approaches to the task of data classification. Experiments performed on large set of paper document images gathered from Internet gave encouraging results.

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