Authors:
Daniel Keysers
1
;
Faisal Shafait
1
and
Thomas M. Breuel
2
Affiliations:
1
German Research Center for Artificial Intelligence (DFKI) GmbH, Germany
;
2
Technical University of Kaiserslautern, Germany
Keyword(s):
Document Image Analysis, Zone Classification.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Feature Extraction
;
Features Extraction
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing, Sensors, Systems Modeling and Control
;
Soft Computing
Abstract:
We describe a simple, fast, and accurate system for document image zone classification — an important sub-problem of document image analysis — that results from a detailed analysis of different features. Using a novel combination of known algorithms, we achieve a very competitive error rate of 1.46% (n = 13811) in comparison to (Wang et al., 2006) who report an error rate of 1.55% (n = 24177) using more complicated techniques. The experiments were performed on zones extracted from the widely used UW-III database, which is representative of images of scanned journal pages and contains ground-truthed real-world data.