Sign Language Recognition Using Leap Motion

Quirós, Luis Quesada; Herrera, Gustavo López; Blanco, Luis Alberto Guerrero

doi:10.1007/978-3-319-26401-1_26

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…The SVM's accuracy was affected by the order in which the sign motions were performed. This result was not validated statistically [42]. Deepali Naglot et al proposed technique for classification of American Sign Language.…”

Section: Sensor-basedmentioning

confidence: 89%

Two Dimensional Convolutional Neural Network Approach for Real-Time Bangla Sign Language Characters Recognition and Translation

et al. 2021

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This paper presents a convolutional neural network (CNN) architecture to recognize Bangla Sign Language (BdSL) characters. The proposed CNN architecture also translates the recognized signs into respective textual Bangla characters and provides real-time prediction. The automatic recognition and translation of sign language make communication between dumb and deaf and normal people natural. The recognition structure is validated with a dataset of a total of 4600 selected hand sign images collected from volunteers using an HD Webcam. The proposed structure guarantees the functionality of enhancing the directed dataset by capturing new images of variant persons in real-time. The proposed model recognizes all the 36 letters and 10 digits of the BdSL with significant accuracy. A state-of-the-art result with a validation accuracy of 99.57% and a validation loss of 0.56% has been achieved in the recognition and translation of the BdSL characters.

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Section: Sensor-basedmentioning

confidence: 89%

Two Dimensional Convolutional Neural Network Approach for Real-Time Bangla Sign Language Characters Recognition and Translation

et al. 2021

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“…From manual gesture to speech, numerous studies using the LMC to recognize finger and hand movements to transcribe sign language into text or speech are listed by Galvá n-Ruiz et al [29]. 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).…”

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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“…Lastly, the leap motion controller connected to a PC, rather than a mobile phone, was used for alphabet sign language recognition. In [9] a recognition rate of 80% was reported for English alphabet, in [10] an accurate recognition of 100% was reported for the same alphabet. For the Indian and Chinese alphabet, recognition rates ranging from 90%-96% are reported in [11] and for the Arabic alphabet, a recognition rate of 99% was reported in [12].…”

Section: Introductionmentioning

confidence: 95%

Android mobile app for real-time bilateral Arabic sign language translation using leap motion controller

Eqab¹,

Shanableh

2017

2017 International Conference on Electrical and Computing Technologies and Applications (ICECTA)

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This paper introduces an android mobile App for real-time bilateral Arabic sign language translation. The system is designed to operate on isolated sign language words. The mobile App has four main components; sign language capture for training, sign language capture for translation, voice to sign language translation and a sign language quiz game. Therefore, the Mobile App can be used for bilateral communication with the deaf community. Acquisition of sign language words is performed using a portable Leap Motion Controller that is connected to the USB of the mobile phone through an OTG adapter. We use simple feature extraction approaches based on statistical features such as mean, variance and covariance. We also use simple classification approach based on the minimum distance classifier. Classification results performed using 15 different sign language words revealed that excellent classification results are achieved. In comparison to a similar system that uses data gloves, our system has the advantage of higher accuracy and the advantage of real-time processing using a mobile App.

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Sign Language Recognition Using Leap Motion

Cited by 8 publications

References 8 publications

Two Dimensional Convolutional Neural Network Approach for Real-Time Bangla Sign Language Characters Recognition and Translation

Two Dimensional Convolutional Neural Network Approach for Real-Time Bangla Sign Language Characters Recognition and Translation

Towards a GUI Gesture Control Using the Leap Motion Controller

Android mobile app for real-time bilateral Arabic sign language translation using leap motion controller

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