Page segmentation using decision integration and wavelet packets

Kamran Etemad, David Doermann, Rama Chellappa

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

Abstract

A new algorithm for layout-independent document page segmentation is suggested. Text, image and graphics regions in a document image are treated as three different "texture" classes. Soft local decisions on small blocks are made using wavelet packet based feature vectors. Segmentation is performed by propagating and integrating soft local decisions over neighboring blocks, within and across scales. The "uncertainties" associated with local decisions are reduced as more contextual evidence is incorporated in the process of decision integration. The majority, taken over weighted combined votes, determines the final decision. The suggested algorithm is based on parallel independent computations which have low complexity. It can also be applied to other signal and image segmentation tasks.

Original languageEnglish (US)
Title of host publicationProceedings of the 12th IAPR International Conference on Pattern Recognition - Conference B
Subtitle of host publicationPattern Recognition and Neural Networks, ICPR 1994
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages345-349
Number of pages5
ISBN (Electronic)0818662700
StatePublished - 1994
Externally publishedYes
Event12th IAPR International Conference on Pattern Recognition - Conference B: Pattern Recognition and Neural Networks, ICPR 1994 - Jerusalem, Israel
Duration: Oct 9 1994Oct 13 1994

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume2
ISSN (Print)1051-4651

Conference

Conference12th IAPR International Conference on Pattern Recognition - Conference B: Pattern Recognition and Neural Networks, ICPR 1994
Country/TerritoryIsrael
CityJerusalem
Period10/9/9410/13/94

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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