Categorization of camera captured documents based on logo identification

Venkata Gopal Edupuganti, Frank Y. Shih, Suryaprakash Kompalli

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, we present a methodology to categorize camera captured documents into pre-defined logo classes. Unlike scanned documents, camera captured documents suffer from intensity variations, partial occlusions, cluttering, and large scale variations. Furthermore, the existence of non-uniform folds and the lack of document being flat make this task more challenging. We present the selection of robust local features and the corresponding parameters by comparisons among SIFT, SURF, MSER, Hessian-affine, and Harris-affine. We evaluate the system not only with respect to amount of space required to store the local features information but also with respect to categorization accuracy. Moreover, the system handles the identification of multiple logos on the document at the same time. Experimental results on a challenging set of real images demonstrate the efficiency of our approach.

Original languageEnglish (US)
Title of host publicationComputer Analysis of Images and Patterns - 14th International Conference, CAIP 2011, Proceedings
Number of pages8
EditionPART 2
StatePublished - 2011
Event14th International Conference on Computer Analysis of Images and Patterns, CAIP 2011 - Seville, Spain
Duration: Aug 29 2011Aug 31 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6855 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other14th International Conference on Computer Analysis of Images and Patterns, CAIP 2011

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


  • Logo detection
  • affine-invariant features
  • clustering
  • hamming embedding


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