文章基本信息

标题：Online handwriting Arabic recognition system using k-nearest neighbors classifier and DCT features
本地全文：下载
作者：Mustafa Ali Abuzaraida ; Mohammed Elmehrek ; Esam Elsomadi 等
期刊名称：International Journal of Electrical and Computer Engineering
电子版ISSN：2088-8708
出版年度：2021
卷号：11
期号：4
页码：3584-3592
DOI：10.11591/ijece.v11i4.pp3584-3592
语种：English
出版社：Institute of Advanced Engineering and Science (IAES)
摘要：With advances in machine learning techniques, handwriting recognition systems have gained a great deal of importance. Lately, the increasing popularity of handheld computers, digital notebooks, and smartphones give 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 were extracted by DCT coefficients and using the k-nearest neighbors (KNN) classifier to classify the segmented 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 the KNN classifier. The availability of an 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.
关键词：arabic script;classification;feature extraction;handwriting recognition;segmentation