A robust and efficient algorithm for bilevel document block classification

Thrasyvoulos N Pappas*, S. H. Tseng, D. A. Kosiba

*Corresponding author for this work

Research output: Contribution to conferencePaper

6 Scopus citations

Abstract

We present a robust and computationally efficient algorithm for the classification of blocks of bilevel machine-printed documents into text and halftone categories. It uses a simple mask that makes use of the different correlation properties between the text and halftone regions, and has comparable or better performance than more sophisticated and computationally intensive spectral analysis techniques. The proposed algorithm is a key component of a document recognition system that segments a document into regions, classifies them into text, halftone, line-art, etc., and then analyzes the regions to obtain a document interpretation. The input data are unusually challenging: multilingual, unoriented (e.g., upside down), and range from ideal (machine-generated) images to very low quality (e.g., copied and FAX-ed) images. We test the proposed algorithm on the University of Washington database and demonstrate its performance on a variety of images from different databases, as well as synthetic images.

Original languageEnglish (US)
Pages1122-1125
Number of pages4
StatePublished - Jan 1 2001
EventIEEE International Conference on Image Processing (ICIP) 2001 - Thessaloniki, Greece
Duration: Oct 7 2001Oct 10 2001

Other

OtherIEEE International Conference on Image Processing (ICIP) 2001
CountryGreece
CityThessaloniki
Period10/7/0110/10/01

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'A robust and efficient algorithm for bilevel document block classification'. Together they form a unique fingerprint.

  • Cite this

    Pappas, T. N., Tseng, S. H., & Kosiba, D. A. (2001). A robust and efficient algorithm for bilevel document block classification. 1122-1125. Paper presented at IEEE International Conference on Image Processing (ICIP) 2001, Thessaloniki, Greece.