A New Segmentation Framework for Arabic Handwritten Text Using Machine Learning Techniques

作     者：Saleem Ibraheem Saleem Adnan Mohsin Abdulazeez Zeynep Orman 

作者机构：Department of Information TechnologyTechnical Informatics College of AkreDuhok Polytechnic UniversityDuhok42004Kurdistan RegionIraq Department of Computer EngineeringFaculty of EngineeringIstanbul University-CerrahpasaIstanbul34320Turkey 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第68卷第8期

页      面：2727-2754页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Writer identification handwritten Arabic biometric systems artificial neural network segmentation skew detection model 

摘      要：The writer identification(WI)of handwritten Arabic text is now of great concern to intelligence agencies following the recent attacks perpetrated by known Middle East terrorist *** is also a useful instrument for the digitalization and attribution of old text to other authors of historic studies,including old national and religious *** this study,we proposed a new affective segmentation model by modifying an artificial neural network model and making it suitable for the binarization stage based on *** modified method is combined with a new effective rotation model to achieve an accurate segmentation through the analysis of the histogram of binary ***,propose a new framework for correct text rotation that will help us to establish a segmentation method that can facilitate the extraction of text from its *** projections and the radon transform are used and improved using machine learning based on a co-occurrence matrix to produce binary *** training stage involves taking a number of images for model *** images are selected randomly with different angles to generate four classes(0–90,90–180,180–270,and 270–360).The proposed segmentation approach achieves a high accuracy of 98.18%.The study ultimately provides two major contributions that are ranked from top to bottom according to the degree of *** proposed method can be further developed as a new application and used in the recognition of handwritten Arabic text from small documents regardless of logical combinations and sentence construction.