Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/12063
Full metadata record
DC FieldValueLanguage
dc.contributor.authorTunçer, Esraen_US
dc.contributor.authorÜnlü, Mehmet Zübeyiren_US
dc.date.accessioned2022-04-29T07:23:50Z-
dc.date.available2022-04-29T07:23:50Z-
dc.date.issued2022en_US
dc.identifier.urihttps://hdl.handle.net/11147/12063-
dc.identifier.urihttps://mijst.mju.ac.th/Vol16/72-88.pdf-
dc.description.abstractA handwritten character recognition methodology based on signals of acceleration obtained from gesture sensors with dynamic time warping (DTW) is presented. After applying the preprocessing steps of filtering, character separation and normalisation, similarities are detected by DTW and each signal component corresponding to a character is classified. However, the nature of the writing process may induce additional time-shifting problems among repetitions of characters since DTW uses only the amplitude values of signals to calculate the distance between them. Accordingly, when signals have different acceleration and deceleration values, irrelevant points of the signals may match each other just because their amplitude values are close. To overcome this problem, derivative dynamic time warping (DDTW) methodology is also implemented. The methodologies mentioned as well as the linear alignment approach were tested with Euclidean, Manhattan and Chessboard distance metrics to detect user-dependent/independent acceleration signals of lower-case characters of the English alphabets and digits. Recognition accuracy rates of Euclidean and Chessboard metrics with DDTW are 98.65%, which is the highest value among all methods applied and metrics. The comparison of Euclidean and Chessboard durations shows that Chessboard with DDTW is the most efficient method in terms of time.-
dc.publisherMaejo Universityen_US
dc.relation.ispartofMaejo International Journal of Science and Technologyen_US
dc.rightsinfo:eu-repo/semantics/openAccess-
dc.subjectCharacter recognitionen_US
dc.subjectThree-axis accelerometeren_US
dc.subjectDynamic time warpingen_US
dc.subjectDerivative dynamic time warpingen_US
dc.titleHandwriting recognition by derivative dynamic time warping methodology via sensor-based gesture recognitionen_US
dc.typeArticleen_US
dc.authorid0000-0001-5027-2408en_US
dc.authorid0000-0003-1605-0160en_US
dc.departmentIzmir Institute of Technology. Electronics and Communication Engineeringen_US
dc.identifier.urlhttps://mijst.mju.ac.th/Vol16/72-88.pdf-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.fulltextWith Fulltext-
item.openairetypeArticle-
item.grantfulltextopen-
crisitem.author.dept03.05. Department of Electrical and Electronics Engineering-
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Files in This Item:
File Description SizeFormat 
Handwriting recognition.pdf1.09 MBAdobe PDFView/Open
Show simple item record

CORE Recommender

Page view(s)

13,100
checked on Jul 4, 2022

Download(s)

3,706
checked on Jul 4, 2022

Google ScholarTM

Check


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.