dc.contributor.author | Saleem, Saleem Ibraheem | |
dc.contributor.author | Abdulazeez, Adnan Mohsin | |
dc.contributor.author | ORMAN, Zeynep | |
dc.date.accessioned | 2021-12-10T12:32:03Z | |
dc.date.available | 2021-12-10T12:32:03Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Saleem S. I. , Abdulazeez A. M. , ORMAN Z., "A New Segmentation Framework for Arabic Handwritten Text Using Machine Learning Techniques", CMC-COMPUTERS MATERIALS & CONTINUA, cilt.68, sa.2, ss.2727-2754, 2021 | |
dc.identifier.issn | 1546-2218 | |
dc.identifier.other | vv_1032021 | |
dc.identifier.other | av_c3351d70-6583-4824-816b-70b6a04a1f7b | |
dc.identifier.uri | http://hdl.handle.net/20.500.12627/174086 | |
dc.identifier.uri | https://doi.org/10.32604/cmc.2021.016447 | |
dc.description.abstract | 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 organizations. It is also a useful instrument for the digitalization and attribution of old text to other authors of historic studies, including old national and religious archives. In 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 blocks. This modified method is combined with a new effective rotation model to achieve an accurate segmentation through the analysis of the histogram of binary images. Also, 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 background. Image projections and the radon transform are used and improved using machine learning based on a co-occurrence matrix to produce binary images. The training stage involves taking a number of images for model training. These 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 importance. The 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. | |
dc.language.iso | eng | |
dc.subject | Computer Science (miscellaneous) | |
dc.subject | BİLGİSAYAR BİLİMİ, BİLGİ SİSTEMLERİ | |
dc.subject | Bilgisayar Bilimi | |
dc.subject | Mühendislik, Bilişim ve Teknoloji (ENG) | |
dc.subject | MALZEME BİLİMİ, MULTIDISCIPLINARY | |
dc.subject | Malzeme Bilimi | |
dc.subject | Bilgisayar Bilimleri | |
dc.subject | Bilgi Güvenliği ve Güvenilirliği | |
dc.subject | Mühendislik ve Teknoloji | |
dc.subject | Metals and Alloys | |
dc.subject | Materials Chemistry | |
dc.subject | General Computer Science | |
dc.subject | General Materials Science | |
dc.subject | Computer Science Applications | |
dc.subject | Information Systems | |
dc.subject | Physical Sciences | |
dc.title | A New Segmentation Framework for Arabic Handwritten Text Using Machine Learning Techniques | |
dc.type | Makale | |
dc.relation.journal | CMC-COMPUTERS MATERIALS & CONTINUA | |
dc.contributor.department | Duhok Polytech Univ , , | |
dc.identifier.volume | 68 | |
dc.identifier.issue | 2 | |
dc.identifier.startpage | 2727 | |
dc.identifier.endpage | 2754 | |
dc.contributor.firstauthorID | 2632977 | |