Features for neural net based region identification of newspaper documents

Tim Andersen, Wei Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

Several features for Neural Network based document region identification are tested. Specifically, this paper examines features for non-text region identification. The Neural Network based region identification algorithm is a key component of a document recognition system that segments a document into regions, classifies them into text, graphic, photo, and other region types, and then uses this classification to guide the processing and analysis of the image. The input data are unusually challenging: low quality images of newspaper documents obtained from microfilmed archives. The results compare favorably with other results reported in the literature.

Original languageEnglish
Title of host publicationProceedings - 7th International Conference on Document Analysis and Recognition, ICDAR 2003
PublisherIEEE Computer Society
Pages403-407
Number of pages5
ISBN (Electronic)0769519601
DOIs
StatePublished - 2003
Event7th International Conference on Document Analysis and Recognition, ICDAR 2003 - Edinburgh, United Kingdom
Duration: 3 Aug 20036 Aug 2003

Publication series

NameProceedings of the International Conference on Document Analysis and Recognition, ICDAR
Volume2003-January
ISSN (Print)1520-5363

Conference

Conference7th International Conference on Document Analysis and Recognition, ICDAR 2003
Country/TerritoryUnited Kingdom
CityEdinburgh
Period3/08/036/08/03

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