Duplicate Detection for Symbolically Compressed Documents

Authors:
Dar-Shyang Lee;Jonathan J. Hull
Affiliations:
-;-
Venue:
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Year:
1999

Citing 0
Cited 6

Imaged Document Text Retrieval Without OCR

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Text-Based Methods for Detecting Duplication in Scanned Document Databases

Information Retrieval
Substitution Deciphering Based on HMMs with Applications to Compressed Document Processing

IEEE Transactions on Pattern Analysis and Machine Intelligence
Duplicate detection in click streams

WWW '05 Proceedings of the 14th international conference on World Wide Web
Towards "intelligent compression" in streams: a biased reservoir sampling based Bloom filter approach

Proceedings of the 15th International Conference on Extending Database Technology
Streaming quotient filter: a near optimal approximate duplicate detection approach for data streams

Proceedings of the VLDB Endowment

Quantified Score

Hi-index	0.00

Visualization

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.