Document image analysis using integrated image and neural processing

Author

Le, Daniel X. ; Thoma, George R. ; Wechsler, Harry

Author_Institution

Nat. Libr. of Med., Bethesda, MD, USA

Volume

1

fYear

1995

fDate

14-16 Aug 1995

Firstpage

327

Abstract

In this paper we present robust algorithms for detecting the page orientation (portrait/landscape) and the degree of skew for binary document images, and a method for classification of binary document images into textual or non-textual data blocks using neural network models. The performance of four neural network models are compared in terms of training times, memory requirements, and classification accuracy, and it was found that the radial basis functions performed best. The experiments show the feasibility of building an integrated document analysis system for page orientation and skew angle detection, and textual block classification

Keywords

document image processing; image classification; neural nets; binary document images; classification; classification accuracy; degree of skew; document image analysis; integrated document analysis system; memory requirements; neural network models; page orientation; radial basis functions; robust algorithms; textual block classification; training times; Back; Biomedical imaging; Computer science; Detection algorithms; Image analysis; Image converters; Libraries; Neural networks; Robustness; Text analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Document Analysis and Recognition, 1995., Proceedings of the Third International Conference on

Conference_Location

Montreal, Que.

Print_ISBN

0-8186-7128-9

Type

conf

DOI

10.1109/ICDAR.1995.599005

Filename

599005