Duplicate detection for symbolically compressed documents

Author

Lee, Dar-Shyang ; Hull, Jonathan J.

Author_Institution

Ricoh Silicon Valley Inc., Menlo Park, CA, USA

fYear

1999

fDate

20-22 Sep 1999

Firstpage

305

Lastpage

308

Abstract

A new family of symbolic compression algorithms has recently been developed that includes the ongoing JBIG2 standardization effort as well as related commercial products. These techniques are specifically designed for binary document images. They cluster individual blobs in a document and store the sequence of occurrence of blobs and representative blob templates, hence the name symbolic compression. This paper describes a method for duplicate detection on symbolically compressed document images. It recognizes the text in an image by deciphering the sequence of occurrence of blobs in the compressed representation. We propose a Hidden Markov Model (HMM) method for solving such deciphering problems and suggest applications in multilingual document duplicate detection

Keywords

hidden Markov models; image matching; standardisation; visual databases; JBIG2 standardization; binary document images; blob templates; deciphering problems; duplicate detection; hidden Markov model; multilingual document duplicate detection; symbolic compression algorithms; symbolically compressed document images; symbolically compressed documents; Clustering algorithms; Compression algorithms; Hidden Markov models; Image coding; Image recognition; Image storage; Pattern matching; Silicon; Standardization; Text recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Document Analysis and Recognition, 1999. ICDAR '99. Proceedings of the Fifth International Conference on

Conference_Location

Bangalore

Print_ISBN

0-7695-0318-7

Type

conf

DOI

10.1109/ICDAR.1999.791785

Filename

791785