AIMC Topic: Sign Language

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Combinatorial Bionic Hierarchical Flexible Strain Sensor for Sign Language Recognition with Machine Learning.

ACS applied materials & interfaces
Flexible strain sensors have been widely researched in fields such as smart wearables, human health monitoring, and biomedical applications. However, achieving a wide sensing range and high sensitivity of flexible strain sensors simultaneously remain...

Real-Time Arabic Sign Language Recognition Using a Hybrid Deep Learning Model.

Sensors (Basel, Switzerland)
Sign language is an essential means of communication for individuals with hearing disabilities. However, there is a significant shortage of sign language interpreters in some languages, especially in Saudi Arabia. This shortage results in a large pro...

Breaking the silence: empowering the mute-deaf community through automatic sign language decoding.

Biomedizinische Technik. Biomedical engineering
OBJECTIVES: The objective of this study is to develop a system for automatic sign language recognition to improve the quality of life for the mute-deaf community in Egypt. The system aims to bridge the communication gap by identifying and converting ...

Efhamni: A Deep Learning-Based Saudi Sign Language Recognition Application.

Sensors (Basel, Switzerland)
Deaf and hard-of-hearing people mainly communicate using sign language, which is a set of signs made using hand gestures combined with facial expressions to make meaningful and complete sentences. The problem that faces deaf and hard-of-hearing peopl...

Synthetic Corpus Generation for Deep Learning-Based Translation of Spanish Sign Language.

Sensors (Basel, Switzerland)
Sign language serves as the primary mode of communication for the deaf community. With technological advancements, it is crucial to develop systems capable of enhancing communication between deaf and hearing individuals. This paper reviews recent sta...

Toward a Vision-Based Intelligent System: A Stacked Encoded Deep Learning Framework for Sign Language Recognition.

Sensors (Basel, Switzerland)
Sign language recognition, an essential interface between the hearing and deaf-mute communities, faces challenges with high false positive rates and computational costs, even with the use of advanced deep learning techniques. Our proposed solution is...

Sign language recognition using the fusion of image and hand landmarks through multi-headed convolutional neural network.

Scientific reports
Sign Language Recognition is a breakthrough for communication among deaf-mute society and has been a critical research topic for years. Although some of the previous studies have successfully recognized sign language, it requires many costly instrume...

Deep Learning Technology to Recognize American Sign Language Alphabet.

Sensors (Basel, Switzerland)
Historically, individuals with hearing impairments have faced neglect, lacking the necessary tools to facilitate effective communication. However, advancements in modern technology have paved the way for the development of various tools and software ...

Signer-Independent Arabic Sign Language Recognition System Using Deep Learning Model.

Sensors (Basel, Switzerland)
Every one of us has a unique manner of communicating to explore the world, and such communication helps to interpret life. Sign language is the popular language of communication for hearing and speech-disabled people. When a sign language user intera...

Application for Recognizing Sign Language Gestures Based on an Artificial Neural Network.

Sensors (Basel, Switzerland)
This paper presents the development and implementation of an application that recognizes American Sign Language signs with the use of deep learning algorithms based on convolutional neural network architectures. The project implementation includes th...