ONLINE HANDWRITING ARABICRECOGNITION SYSTEM USINGK-NEAREST NEIGHBORS CLASSIFIER AND DCT FEATURES

Mustafa Ali Abuzaraida, Mohammed Elmehrek, Esam Elsomadi

Abstract


With advances in machine learning techniques, handwriting recognition systems has gained a great deal of importance. Lately, the increasing popularity of handheld computers, digital notebooks, and smart phones gives the field of online handwriting recognition more interest. In this paper, we propose an enhanced method for the recognition of Arabic handwriting words using a directions-based segmentation technique and Discrete Cosine Transform (DCT) coefficients as structural features. The main contribution of this research was combining a total of 18 structural features which was extracted by DCT coefficients and using the K-Nearest Neighbors (KNN) classifier to classify the segemented characters based on the extracted features. A dataset is used to validate the proposed method consisting of 2500 words in total. The obtained average 99.10% accuracy in recognition of handwritten characters shows that the proposed approach, through its multiple phases, is efficient in separating, distinguishing, and classifying Arabic handwritten characters using KNN classifier. The availability of online dataset of Arabic handwriting words is the main issue in this field. However, the dataset used will be available for research via the website.

Keywords


Arabic Script; Classification; Feature Extraction; Handwriting Recognition; Segmentation



DOI: http://doi.org/10.11591/ijece.v11i4.pp%25p
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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578