Authors:
Mehmet Ahat
;
Cagdas Ulas
and
Onur Agin
Affiliation:
Yapi Kredi Bank, Turkey
Keyword(s):
Document Image Retrieval and Classification, SVM, One-Versus-All, AdaBoost, ECOC, BoVW Model.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer-Supported Education
;
Data Manipulation
;
Domain Applications and Case Studies
;
Fuzzy Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Image Processing and Artificial Vision Applications
;
Industrial, Financial and Medical Applications
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Supervised and Unsupervised Learning
;
Support Vector Machines and Applications
;
Theory and Methods
Abstract:
In this paper, we describe easily extractable features and an approach for document image retrieval and classification
at spatial level. The approach is based on the content of the image and utilizing visual similarity,
it provides high speed classification of noisy text document images without optical character recognition
(OCR). Our method involves a bag-of-visual words (BoVW) model on the designed descriptors and a Random-
Window (RW) technique to capture the structural relationships of the spatial layout. Using the features based
on these information, we analyze different multiclass classification methods as well as ensemble classifiers
method with Support Vector Machine (SVM) as a base learner. The results demonstrate that the proposed
method for obtaining structural relations is competitive for noisy document image categorization.