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Writer Identification Based on Arabic Handwriting Recognition by using Speed Up Robust Feature and K- Nearest Neighbor Classification

Domain:

natural language processing

Record type:

paper
Creator:
AliBasAsm
Publisher:
Uni
Host:
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).

Visit

doi.org

Tasks

computer visionoptical character recognition

Licenses

https://www.journalofbabylon.com/index.php/JUBPAS/CopyrightandLicensing

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