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

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Computer Analysis of Images and Patterns (CAIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9257))

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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.

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Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, Żołnierska Str. 52, 71–210, Szczecin, Poland

    Andrzej Markiewicz & Paweł Forczmański

Authors
  1. Andrzej Markiewicz
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  2. Paweł Forczmański
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Corresponding author

Correspondence to Paweł Forczmański .

Editor information

Editors and Affiliations

  1. University of Malta, Msida, Malta

    George Azzopardi

  2. University of Groningen, Groningen, The Netherlands

    Nicolai Petkov

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Markiewicz, A., Forczmański, P. (2015). Detection and Classification of Interesting Parts in Scanned Documents by Means of AdaBoost Classification and Low-Level Features Verification. In: Azzopardi, G., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2015. Lecture Notes in Computer Science(), vol 9257. Springer, Cham. https://doi.org/10.1007/978-3-319-23117-4_46

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  • DOI: https://doi.org/10.1007/978-3-319-23117-4_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23116-7

  • Online ISBN: 978-3-319-23117-4

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