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Combination of multiple classifiers for off-line handwritten Arabic word recognition
Joint Authors
Zaghdoudi, Rashid
Seridi, Hamid
Source
The International Arab Journal of Information Technology
Issue
Vol. 14, Issue 5 (30 Sep. 2017)8 p.
Publisher
Publication Date
2017-09-30
Country of Publication
Jordan
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Abstract EN
This study investigates the combination of different classifiers to improve Arabic handwritten word recognition.
Features based on Discrete Cosine Transform (DCT) and Histogram of Oriented Gradients (HOG) are computed to represent the handwritten words.
The dimensionality of the HOG features is reduced by applying Principal Component Analysis (PCA).
Each set of features is separately fed to two different classifiers, support vector machine (SVM) and fuzzy k-nearest neighbor (FKNN) giving a total of four independent classifiers.
A set of different fusion rules is applied to combine the output of the classifiers.
The proposed scheme evaluated on the IFN/ENIT database of Arabic handwritten words reveal that combining the classifiers results in improved recognition rates which, in some cases, outperform the state-of-the-art recognition systems.
American Psychological Association (APA)
Zaghdoudi, Rashid& Seridi, Hamid. 2017. Combination of multiple classifiers for off-line handwritten Arabic word recognition. The International Arab Journal of Information Technology،Vol. 14, no. 5.
https://search.emarefa.net/detail/BIM-852274
Modern Language Association (MLA)
Zaghdoudi, Rashid& Seridi, Hamid. Combination of multiple classifiers for off-line handwritten Arabic word recognition. The International Arab Journal of Information Technology Vol. 14, no. 5 (Sep. 2017).
https://search.emarefa.net/detail/BIM-852274
American Medical Association (AMA)
Zaghdoudi, Rashid& Seridi, Hamid. Combination of multiple classifiers for off-line handwritten Arabic word recognition. The International Arab Journal of Information Technology. 2017. Vol. 14, no. 5.
https://search.emarefa.net/detail/BIM-852274
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-852274