Arabic (Indian) handwritten digits recognition using multi feature and KNN classifier
Author
Source
Journal of Babylon University : Journal of Applied and Pure Sciences
Issue
Vol. 26, Issue 4 (30 Apr. 2018), pp.10-17, 8 p.
Publisher
Publication Date
2018-04-30
Country of Publication
Iraq
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Abstract EN
This paper presents an Arabic (Indian) handwritten digit recognition system based on combining multi feature extraction methods, such a upper_lower profile, Vertical _ Horizontal projection and Discrete Cosine Transform (DCT) with Standard Deviation σi called (DCT_SD) methods.
These features are extracted from the image after dividing it by several blocks.
KNN classifier used for classification purpose.
This work is tested with the ADBase standard database (Arabic numerals), which consist of 70,000 digits were 700 different writers write it.
In proposing system used 60000 digits, images for training phase and 10000 digits, images in testing phase.
This work achieved 97.32% recognition Accuracy.
American Psychological Association (APA)
Abd al-Hasan, Alya Karim. 2018. Arabic (Indian) handwritten digits recognition using multi feature and KNN classifier. Journal of Babylon University : Journal of Applied and Pure Sciences،Vol. 26, no. 4, pp.10-17.
https://search.emarefa.net/detail/BIM-1093485
Modern Language Association (MLA)
Abd al-Hasan, Alya Karim. Arabic (Indian) handwritten digits recognition using multi feature and KNN classifier. Journal of Babylon University : Journal of Applied and Pure Sciences Vol. 26, no. 4 (2018), pp.10-17.
https://search.emarefa.net/detail/BIM-1093485
American Medical Association (AMA)
Abd al-Hasan, Alya Karim. Arabic (Indian) handwritten digits recognition using multi feature and KNN classifier. Journal of Babylon University : Journal of Applied and Pure Sciences. 2018. Vol. 26, no. 4, pp.10-17.
https://search.emarefa.net/detail/BIM-1093485
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 17
Record ID
BIM-1093485