Skip navigation
Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/15936
Full metadata record
DC FieldValueLanguage
dc.contributor.authorAgarwal C.-
dc.contributor.authorDogra D.P.-
dc.contributor.authorSaini R.-
dc.contributor.authorPratim Roy, Partha-
dc.date.accessioned2020-12-02T11:42:02Z-
dc.date.available2020-12-02T11:42:02Z-
dc.date.issued2016-
dc.identifier.citationProceedings of 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015, (2016), 539- 543-
dc.identifier.isbn9.78148E+12-
dc.identifier.urihttps://doi.org/10.1109/ACPR.2015.7486561-
dc.identifier.urihttp://repository.iitr.ac.in/handle/123456789/15936-
dc.description.abstractIn this paper, we present a word extraction and recognition methodology from online cursive handwritten text-lines recorded by Leap motion controller The online text, drawn by 3D gesture in air, is distinct from usual online pen-based strokes. The 3D gestures are recorded in air, hence they produce often non-uniform text style and jitter-effect while writing. Also, due to the constraint of writing in air, the pause of stroke-flow between words is missing. Instead all words and lines are connected by a continuous stroke. In this paper, we have used a simple but effective heuristic to segment words written in air. Here, we propose a segmentation methodology of continuous 3D strokes into text-lines and words. Separation of text lines is achieved by heuristically finding the large gap-information between end and start-positions of successive text lines. Word segmentation is characterized in our system as a two class problem. In the next phase, we have used Hidden Markov Model-based approach to recognize these segmented words. Our experimental validation with a large dataset consisting with 320 sentences reveals that the proposed heuristic based word segmentation algorithm performs with accuracy as high as 80.3%c and an accuracy of 77.6% has been recorded by HMM-based word recognition when these segmented words are fed to HMM. The results show that the framework is efficient even with cluttered gestures. © 2015 IEEE.-
dc.description.sponsorshipFUSIONEX-
dc.language.isoen_US-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.relation.ispartofProceedings of 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015-
dc.subjectComputational linguistics-
dc.subjectHidden Markov models-
dc.subjectMarkov processes-
dc.subjectPattern recognition-
dc.subjectExperimental validations-
dc.subjectHandwritten texts-
dc.subjectJitter effect-
dc.subjectLarge dataset-
dc.subjectMotion controller-
dc.subjectNon-uniform-
dc.subjectWord recognition-
dc.subjectWord segmentation-
dc.subjectCharacter recognition-
dc.titleSegmentation and recognition of text written in 3D using Leap motion interface-
dc.typeConference Paper-
dc.scopusid57190285266-
dc.scopusid35408975400-
dc.scopusid57190288840-
dc.scopusid56880478500-
dc.affiliationAgarwal, C., Department of CSE, NIT Rourkela, India-
dc.affiliationDogra, D.P., School of Electrical Sciences, IIT Bhubaneswar, India-
dc.affiliationSaini, R., Department of CSE, IIT Roorkee, India-
dc.affiliationRoy, P.P., Department of CSE, IIT Roorkee, India-
dc.identifier.conferencedetails3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015, 3-6 November 2016-
Appears in Collections:Conference Publications [CS]

Files in This Item:
There are no files associated with this item.
Show simple item record


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