Hand sign recognition from depth images with multi-scale density features for deaf mute persons

Sahana, Taniya; Paul, Soumi; Basu, Subhadip; Mollah, Ayatullah Faruk

doi:10.1016/j.procs.2020.03.243

Cited by 21 publications

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A hand gesture recognition method for sign language communication, demonstrating promising results with a high recognition rate, was proposed in Sahana et al (2020). The research advances the interface between individuals with speech and hearing disabilities and computer systems, facilitating improved communication and accessibility.…”

Section: Machine Learning-based Techniquesmentioning

confidence: 99%

Silent no more: a comprehensive review of artificial intelligence, deep learning, and machine learning in facilitating deaf and mute communication

ZainEldin,

Gamel,

Talaat

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

People who often communicate via sign language are essential to our society and significantly contribute. They struggle with communication mostly because other people, who often do not understand sign language, cannot interact with them. It is necessary to develop a dependable system for automatic sign language recognition. This paper aims to provide a comprehensive review of the advancements in artificial intelligence (AI), deep learning (DL), and machine learning (ML) technologies that have been used to facilitate communication for individuals who are deaf and mute (D–M). This study explores various applications of these technologies, including sign language interpretation, speech recognition, and text-to-speech synthesis. By examining the current state of research and development in AI, ML, and DL for the D–M field, the survey sheds light on the potential and challenges faced in utilizing AI, deep learning, and ML to bridge the communication gap for the D–M community. The findings of this survey will contribute to a greater understanding of the potential impact of these technologies in improving access to communication for individuals who are D–M, thereby aiding in the development of more inclusive and accessible solutions.

show abstract

Section: Machine Learning-based Techniquesmentioning

confidence: 99%

Silent no more: a comprehensive review of artificial intelligence, deep learning, and machine learning in facilitating deaf and mute communication

ZainEldin,

Gamel,

Talaat

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

show abstract

“…The gesture of a hand and the location of its fingertips are essential information for a computer to understand the state of the interaction medium. Recognizing hand gestures is equally important to interpret sign language [5,6,7,8,9]. Moreover, in virtual reality (VR) and mixed reality (MR) environments, the recognition of hand gestures, and detection of fingertips are essential to interact with the virtual environment [10,11,12,13,14].…”

Section: Introductionmentioning

confidence: 99%

Unified Learning Approach for Egocentric Hand Gesture Recognition and Fingertip Detection

Alam,

Islam,

Rahman

2021

Preprint

View full text Add to dashboard Cite

In human-computer interaction or sign language interpretation, recognizing hand gestures and detecting fingertips become ubiquitous in computer vision research. In this paper, a unified approach of convolutional neural network for both hand gesture recognition and fingertip detection is introduced. The proposed algorithm uses a single network to predict the probabilities of finger class and positions of fingertips in one forward propagation of the network. Instead of directly regressing the positions of fingertips from the fully connected layer, the ensemble of the position of fingertips is regressed from the fully convolutional network. Subsequently, the ensemble average is taken to regress the final position of fingertips. Since the whole pipeline uses a single network, it is significantly fast in computation. The proposed method results in remarkably less pixel error as compared to that in the direct regression approach and it outperforms the existing fingertip detection approaches including the Heatmap-based framework.

show abstract

An Approach to Sri Lankan Sign Language Recognition Using Deep Learning with MediaPipe

Herath,

Ishanka

2022

Digital Technologies and Applications

View full text Add to dashboard Cite

Hand sign recognition from depth images with multi-scale density features for deaf mute persons

Cited by 21 publications

References 23 publications

Silent no more: a comprehensive review of artificial intelligence, deep learning, and machine learning in facilitating deaf and mute communication

Silent no more: a comprehensive review of artificial intelligence, deep learning, and machine learning in facilitating deaf and mute communication

Unified Learning Approach for Egocentric Hand Gesture Recognition and Fingertip Detection

An Approach to Sri Lankan Sign Language Recognition Using Deep Learning with MediaPipe

Contact Info

Product

Resources

About