Categorization of camera captured documents based on logo identification

Document Type

Conference Proceeding

Publication Date

9-20-2011

Abstract

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. © 2011 Springer-Verlag.

Identifier

80052812318 (Scopus)

ISBN

[9783642236778]

Publication Title

Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics

External Full Text Location

https://doi.org/10.1007/978-3-642-23678-5_14

e-ISSN

16113349

ISSN

03029743

First Page

130

Last Page

137

Issue

PART 2

Volume

6855 LNCS

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