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

  • Andrzej Markiewicz
  • Paweł ForczmańskiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9257)

Abstract

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.

Keywords

Local Binary Pattern Document Image Text Detection Page Segmentation Rotation Invariant Texture 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of Computer Science and Information TechnologyWest Pomeranian University of TechnologySzczecinPoland

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