Writer Identification Based on Arabic Handwriting Recognition by using Speed Up Robust Feature and K- Nearest Neighbor Classification
| Main Authors: | Abdul Hassan, Alia Karim, Mahdi, Bashar Saadoon, Mohammed, Asmaa Abdullah |
|---|---|
| Format: | Article info application/pdf Journal |
| Bahasa: | eng |
| Terbitan: |
University of Babylon
, 2019
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| Subjects: | |
| Online Access: |
https://www.journalofbabylon.com/index.php/JUBPAS/article/view/2060 https://www.journalofbabylon.com/index.php/JUBPAS/article/view/2060/1605 |
Daftar Isi:
- In a writer recognition system, the system performs a “one-to-many” search in a large database with handwriting samples of known authors and returns a possible candidate list. This paper proposes method for writer identification handwritten Arabic word without segmentation to sub letters based on feature extraction speed up robust feature transform (SURF) and K nearest neighbor classification (KNN) to enhance the writer's identification accuracy. After feature extraction, it can be cluster by K-means algorithm to standardize the number of features. The feature extraction and feature clustering called to gather Bag of Word (BOW); it converts arbitrary number of image feature to uniform length feature vector. The proposed method experimented using (IFN/ENIT) database. The recognition rate of experiment result is (96.666).