<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE ArticleSet PUBLIC "-//NLM//DTD PubMed 2.7//EN" "https://dtd.nlm.nih.gov/ncbi/pubmed/in/PubMed.dtd">
<ArticleSet>
		<Article>
		<Journal>
			<PublisherName>Majlesi Journal of Electrical Engineering</PublisherName>
			<JournalTitle>A Novel Method for Persian Handwritten Digit Recognition Using Support Vector Machine</JournalTitle>
			<Issn></Issn>
			<Volume>Volume 12 (2018)</Volume>
			<Issue>Issue 3, September 2018</Issue>
			<PubDate PubStatus="epublish">
                <Year>2024</Year>
                <Month>02</Month>
                <Day>15</Day>
			</PubDate>
		</Journal>
		<ArticleTitle>A Novel Method for Persian Handwritten Digit Recognition Using Support Vector Machine</ArticleTitle>
		<VernacularTitle></VernacularTitle>
		<FirstPage></FirstPage>
		<LastPage></LastPage>
		<ELocationID EIdType="doi"></ELocationID>
		<Language>EN</Language>
		<AuthorList>
            			<Author>
                				<FirstName>Mojtaba</FirstName>
				<LastName>Mohammadpoor</LastName>
				<Affiliation>Electrical and Computer Engineering Department, University of  Gonabad, Gonabad, Iran,</Affiliation>
				<Identifier Source="ORCID"></Identifier>
			</Author>
            			<Author>
                				<FirstName>Abbas</FirstName>
				<LastName>Mehdizadeh</LastName>
				<Affiliation>Department of Electrical and Computer Engineering, University of Gonabad, Gonabad</Affiliation>
				<Identifier Source="ORCID"></Identifier>
			</Author>
            			<Author>
                				<FirstName>Hava</FirstName>
				<LastName>Alizadeh Noghabi</LastName>
				<Affiliation>Department of Computing, Nilai University, Negeri Sembilan</Affiliation>
				<Identifier Source="ORCID"></Identifier>
			</Author>
            		</AuthorList>
		<PublicationType>Journal Article</PublicationType>
		<History>
			<PubDate PubStatus="received">
				<Year>2024</Year>
				<Month>02</Month>
				<Day>15</Day>
			</PubDate>
		</History>
		<Abstract>Handwritten digit recognition has got a special role in different applications in the field of digital recognition including; handwritten address detection, check, and document. Persian handwritten digits classification has been facing difficulties due to different handwritten styles, inter-class similarities, and intra-class differences.  In this paper, a novel method for detecting Persian handwritten digits is presented. In the proposed method, a combination of Histogram of Oriented Gradients (HOG), 4-side profiles of the digit image, and some horizontal and vertical samples was used and the dimension of the feature vector was reduced using Principal Component Analysis (PCA). The proposed method applied to the HODA database, and Support Vector Machine (SVM) was used in the classification step. Results revealed that the detection accuracy of such method has 99% accuracy with an adequate rate due to existing unacceptable samples in the database, therefore, the proposed method could improve the outcomes compared to other existing methods.</Abstract>
		<ObjectList>
            			<Object Type="keyword">
				<Param Name="value">Histogram of oriented gradients (HOG)</Param>
			</Object>
						<Object Type="keyword">
				<Param Name="value">Principle component analysis (PCA)</Param>
			</Object>
						<Object Type="keyword">
				<Param Name="value">Support vector machine (SVM)</Param>
			</Object>
					</ObjectList>
	</Article>
	</ArticleSet>
