ANN based Indian Sign Language numerals recognition using the leap motion controller

Naglot, Deepali; Kulkarni, Milind

doi:10.1109/inventive.2016.7824830

Cited by 25 publications

(6 citation statements)

References 8 publications

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“…Further simple position thresholding [ 54 , 126 ] is also used to perform discrete inputs. Interaction with scene objects is realised by grabbing or grasping [ 113 , 198 , 199 , 200 ]. Here selection is recognised by collision detection between the object of interaction (e.g., a virtual hand model) and the scene object.…”

Section: Methodsmentioning

confidence: 99%

“…General SLTC-applications are presented, for example, by Fok et al [ 107 ] or Kumar et al [ 108 ]. Country-specific recognition approaches are available amongst others for the American [ 109 ], Australian [ 110 ], Arabic [ 111 ], Greek [ 112 ], Indian [ 113 ] and Mexican [ 114 ] sign language. An evaluation of different potential solutions for recognition, translation and representation of sign language for e-learning platforms has been conducted by Martins et al [ 115 ].…”

Section: Applications and Contextsmentioning

confidence: 99%

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Review of Three-Dimensional Human-Computer Interaction with Focus on the Leap Motion Controller

Bachmann

Weichert

Rinkenauer

2018

Sensors

126

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Modern hardware and software development has led to an evolution of user interfaces from command-line to natural user interfaces for virtual immersive environments. Gestures imitating real-world interaction tasks increasingly replace classical two-dimensional interfaces based on Windows/Icons/Menus/Pointers (WIMP) or touch metaphors. Thus, the purpose of this paper is to survey the state-of-the-art Human-Computer Interaction (HCI) techniques with a focus on the special field of three-dimensional interaction. This includes an overview of currently available interaction devices, their applications of usage and underlying methods for gesture design and recognition. Focus is on interfaces based on the Leap Motion Controller (LMC) and corresponding methods of gesture design and recognition. Further, a review of evaluation methods for the proposed natural user interfaces is given.

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Section: Methodsmentioning

confidence: 99%

Section: Applications and Contextsmentioning

confidence: 99%

Review of Three-Dimensional Human-Computer Interaction with Focus on the Leap Motion Controller

Bachmann

Weichert

Rinkenauer

2018

Sensors

126

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“…The signer's training is a key factor in the sign recognition process and the order in which sign movements are performed affects accuracy [30] (American Sign Language). The LMC is also used for the recognition of other sign languages [31] (Arabic Sign Language), [32] (Chinese Sign Language), [33] (Indian Sign Language). In addition to speech, Škraba et al [34] expect to use the LMC to pilot a wheelchair.…”

Section: The Lmc As An Assistive Technologymentioning

confidence: 99%

Towards a GUI Gesture Control Using the Leap Motion Controller

Vannobel¹,

Bekaert²,

Baumann³

2022

MMC_C

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The Leap Motion Controller is a small hand motion capture device that has been the subject of numerous studies since it was launched in 2013. This sensor is particularly used in the field of disability, diagnosis or functional rehabilitation. This article presents two preliminary studies on the use of this device as a gesture mouse and then as a gesture controller. If the Leap Motion Controller is simple to implement, its lack of precision in some gesture configurations and of reliability (outliers) is a barrier which could certainly be lifted in the context of a multimodal interaction.

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“…In [ 10 ], 10 handshapes corresponding to the digits in Indian Sign Language were recognized. The feature vector consisted of the distances between the consecutive fingertips and palm center and the distances between the fingertips.…”

Section: Recent Workmentioning

confidence: 99%

Handshape Recognition Using Skeletal Data

Kapuściński

Organisciak

2018

Sensors

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In this paper, a method of handshapes recognition based on skeletal data is described. A new feature vector is proposed. It encodes the relative differences between vectors associated with the pointing directions of the particular fingers and the palm normal. Different classifiers are tested on the demanding dataset, containing 48 handshapes performed 500 times by five users. Two different sensor configurations and significant variation in the hand rotation are considered. The late fusion at the decision level of individual models, as well as a comparative study carried out on a publicly available dataset, are also included.

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ANN based Indian Sign Language numerals recognition using the leap motion controller

Cited by 25 publications

References 8 publications

Review of Three-Dimensional Human-Computer Interaction with Focus on the Leap Motion Controller

Review of Three-Dimensional Human-Computer Interaction with Focus on the Leap Motion Controller

Towards a GUI Gesture Control Using the Leap Motion Controller

Handshape Recognition Using Skeletal Data

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