Sign Language Recognition Techniques- A Review

Safeel, Mohammed; Sukumar, Tejas; Shashank, K; Arman, M D; Shashidhar, R; Puneeth, S B

doi:10.1109/inocon50539.2020.9298376

Cited by 25 publications

(8 citation statements)

References 23 publications

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“…The breakthroughs in sensorial devices and AI have paved the way for the development of sign language applications that can immensely facilitate hearing-impaired people in their everyday life. Previous literature reviews mainly concentrate on specific sign language technologies, such as video-based and sensor-based sign language recognition [3][4][5][6][7] and sign language translation [8,9]. Lately, with the development of sign language applications, there are also reviews that presented sign language systems to facilitate hearing-impaired people in teaching and learning, as well as in voice and text interpretation systems [10,11].…”

Section: Introductionmentioning

confidence: 99%

“…Previous literature reviews mainly concentrate on specific sign language technologies, such as video-based and sensor-based sign language recognition [ 3 , 4 , 5 , 6 , 7 ] and sign language translation [ 8 , 9 ]. Lately, with the development of sign language applications, there are also reviews that presented sign language systems to facilitate hearing-impaired people in teaching and learning, as well as in voice and text interpretation systems [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence Technologies for Sign Language

Papastratis

Chatzikonstantinou

Konstantinidis

et al. 2021

Sensors

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AI technologies can play an important role in breaking down the communication barriers of deaf or hearing-impaired people with other communities, contributing significantly to their social inclusion. Recent advances in both sensing technologies and AI algorithms have paved the way for the development of various applications aiming at fulfilling the needs of deaf and hearing-impaired communities. To this end, this survey aims to provide a comprehensive review of state-of-the-art methods in sign language capturing, recognition, translation and representation, pinpointing their advantages and limitations. In addition, the survey presents a number of applications, while it discusses the main challenges in the field of sign language technologies. Future research direction are also proposed in order to assist prospective researchers towards further advancing the field.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Technologies for Sign Language

Papastratis

Chatzikonstantinou

Konstantinidis

et al. 2021

Sensors

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show abstract

“…The sign language gestures can be segmented in images using a global thresholding algorithm (in a binary image), infrared illumination based (camera provides a distance), software based (color separation), HSV (hue, saturation, lightness) brightness factor matching and Binary Large OBject method [5].…”

Section: Introductionmentioning

confidence: 99%

“…The feature extraction can be achieved through Edge Detection using HSV brightness matching, palm point detection, center of region detection, dimensionality reduction, scale-invariant feature transform technique and leap motion [5].…”

Section: Introductionmentioning

confidence: 99%

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Towards Portuguese Sign Language Identification Using Deep Learning

Costa

Martinho

Vardasca

2021

Communications in Computer and Information Science

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In Portugal there are above 80,000 people with hearing impairment with the need to communicate through the sign language. Equal opportunities and social inclusion are the major concerns of the current society. It is aim of this research to create and evaluate a Deep Learning model that using a dataset with images of characters in Portuguese sign language can identify the gesture of a user, recognizing it. For model training, 5826 representative samples of the characters 'C', 'I', 'L', 'U' and 'Y' in Portuguese sign language. The Deep Learning model is based on a convolutional neural network. The model evaluated using the sample allowed for an accuracy of 98.5%, which is considered as a satisfactory result. However, there are two gaps: the existence of datasets with the totality of the AQ1 alphabet in the Portuguese sign language and with the various representations of movement that each word has at the layout of letters. Using the proposed model with more complete datasets would allow to develop more inclusive user interfaces and equal opportunities for users with auditory difficulties.

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Skeleton-Based Sign Language Recognition with Graph Convolutional Networks on Small Data

Nakamura

Jing

2022

HCI International 2022 - Late Breaking Papers. Multimodality in Advanced Interaction Environments

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Sign Language Recognition Techniques- A Review

Cited by 25 publications

References 23 publications

Artificial Intelligence Technologies for Sign Language

Artificial Intelligence Technologies for Sign Language

Towards Portuguese Sign Language Identification Using Deep Learning

Skeleton-Based Sign Language Recognition with Graph Convolutional Networks on Small Data

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