Share Email Print

Proceedings Paper

Trainable multiscript orientation detection
Author(s): Joost Van Beusekom; Yves Rangoni; Thomas M. Breuel
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Detecting the correct orientation of document images is an important step in large scale digitization processes, as most subsequent document analysis and optical character recognition methods assume upright position of the document page. Many methods have been proposed to solve the problem, most of which base on ascender to descender ratio computation. Unfortunately, this cannot be used for scripts having no descenders nor ascenders. Therefore, we present a trainable method using character similarity to compute the correct orientation. A connected component based distance measure is computed to compare the characters of the document image to characters whose orientation is known. This allows to detect the orientation for which the distance is lowest as the correct orientation. Training is easily achieved by exchanging the reference characters by characters of the script to be analyzed. Evaluation of the proposed approach showed accuracy of above 99% for Latin and Japanese script from the public UW-III and UW-II datasets. An accuracy of 98.9% was obtained for Fraktur on a non-public dataset. Comparison of the proposed method to two methods using ascender / descender ratio based orientation detection shows a significant improvement.

Paper Details

Date Published: 18 January 2010
PDF: 8 pages
Proc. SPIE 7534, Document Recognition and Retrieval XVII, 75340W (18 January 2010); doi: 10.1117/12.839409
Show Author Affiliations
Joost Van Beusekom, German Research Ctr. for Artificial Intelligence GmbH (Germany)
Technical Univ. of Kaiserslautern (Germany)
Yves Rangoni, German Research Ctr. for Artificial Intelligence GmbH (Germany)
Thomas M. Breuel, German Research Ctr. for Artificial Intelligence GmbH (Germany)
Technical Univ. of Kaiserslautern (Germany)

Published in SPIE Proceedings Vol. 7534:
Document Recognition and Retrieval XVII
Laurence Likforman-Sulem; Gady Agam, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?