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dc.contributor.authorGattal, A.
dc.contributor.authorDjeddi, C.
dc.contributor.authorJamil, A.
dc.contributor.authorBensefia, A.
dc.date.accessioned2022-03-04T19:12:35Z
dc.date.available2022-03-04T19:12:35Z
dc.date.issued2021
dc.identifier.isbn9783030736880
dc.identifier.issn2194-5357
dc.identifier.urihttps://doi.org/10.1007/978-3-030-73689-7_28
dc.identifier.urihttps://hdl.handle.net/20.500.12436/3248
dc.description12th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2020 and 16th International Conference on Information Assurance and Security, IAS 2020 -- 15 December 2020 through 18 December 2020 --en_US
dc.description.abstractHandwritten digits recognition is a key research problem in the domain of image analysis and pattern recognition. Specifically, the appearance approaches based on feature extraction have been proposed to solve many research issues. This paper presents a novel way of extending the oriented Basic Image Features column (oBIFs column) to multi-scale features. Moreover, this method is very efficient and robust for hand handwritten digit recognition as the changes in size, slant and shape have little effect on the accuracy. The proposed method is carried in two steps: first, we concatenate oBIF image for scale parameter at two or more scales to provide Multi-scale oBIFs. Second, the two variation Multi-Scale oBIFs are crossed to form columns at each location. Finally, a histogram is created from the occurrences of different patterns. The CVL dataset was considered to evaluate our approach, where the digits used where from different fonts, widths, and directions. We managed to achieve a good recognition of unnormalized digits in almost 96% of the cases, which is comparable with the state-of-the-art approaches. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.en_US
dc.language.isoengen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofAdvances in Intelligent Systems and Computingen_US
dc.identifier.doi10.1007/978-3-030-73689-7_28
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCVL dataseten_US
dc.subjectHandwritten digits recognitionen_US
dc.subjectMulti-Scale oBIFs Columnen_US
dc.subjectSoft computingen_US
dc.subjectHandwritten digit recognitionen_US
dc.subjectHandwritten digits recognitionen_US
dc.subjectImage featuresen_US
dc.subjectMulti-scale featuresen_US
dc.subjectResearch issuesen_US
dc.subjectResearch problemsen_US
dc.subjectScale parameteren_US
dc.subjectState-of-the-art approachen_US
dc.subjectCharacter recognitionen_US
dc.titleMulti-Scale Oriented Basic Image Features Column for Handwritten Digit Recognitionen_US
dc.typeconferenceObjecten_US
dc.departmentİZÜen_US
dc.identifier.volume1383 AISCen_US
dc.identifier.startpage289en_US
dc.identifier.endpage298en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.department-tempGattal, A., Department of Mathematics and Computer Science, Larbi Tebessi University, Tebessa, Algeria; Djeddi, C., Department of Mathematics and Computer Science, Larbi Tebessi University, Tebessa, Algeria; Jamil, A., Department of Computer Engineering, Istanbul Sabahattin Zaim University, Istanbul, Turkey; Bensefia, A., Higher Colleges of Technology CIS Division, Abu Dhabi, United Arab Emiratesen_US
dc.authorscopusid50861074100
dc.authorscopusid55078188200
dc.authorscopusid49863650600
dc.authorscopusid56026467900
dc.identifier.scopus2-s2.0-85105887468en_US


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