A comprehensive survey and taxonomy of sign language research

El-Alfy, El-Sayed M.; Luqman, Hamzah

doi:10.1016/j.engappai.2022.105198

Cited by 30 publications

(15 citation statements)

References 146 publications

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“…Although LSTM and GRU are efficient for learning time-series data, these algorithms lack the capability of CNN for learning the spatial features of the input data [ 146 ]. To overcome this problem, several researchers employed CNN as a feature extractor and fed these features into a time-series learning technique, such as LSTM or GRU.…”

Section: Resultsmentioning

confidence: 99%

A systematic review of machine learning techniques for stance detection and its applications

Alturayeif

Luqman

Ahmed

2023

Neural Comput & Applic

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Stance detection is an evolving opinion mining research area motivated by the vast increase in the variety and volume of user-generated content. In this regard, considerable research has been recently carried out in the area of stance detection. In this study, we review the different techniques proposed in the literature for stance detection as well as other applications such as rumor veracity detection. Particularly, we conducted a systematic literature review of empirical research on the machine learning (ML) models for stance detection that were published from January 2015 to October 2022. We analyzed 96 primary studies, which spanned eight categories of ML techniques. In this paper, we categorize the analyzed studies according to a taxonomy of six dimensions: approaches, target dependency, applications, modeling, language, and resources. We further classify and analyze the corresponding techniques from each dimension’s perspective and highlight their strengths and weaknesses. The analysis reveals that deep learning models that adopt a mechanism of self-attention have been used more frequently than the other approaches. It is worth noting that emerging ML techniques such as few-shot learning and multitask learning have been used extensively for stance detection. A major conclusion of our analysis is that despite that ML models have shown to be promising in this field, the application of these models in the real world is still limited. Our analysis lists challenges and gaps to be addressed in future research. Furthermore, the taxonomy presented can assist researchers in developing and positioning new techniques for stance detection-related applications.

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Section: Resultsmentioning

confidence: 99%

A systematic review of machine learning techniques for stance detection and its applications

Alturayeif

Luqman

Ahmed

2023

Neural Comput & Applic

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“…In fact, the availability of sign language databases is limited, which is one of the most significant issues facing sign language recognition and translation systems. Finding a dataset that has manual and non-manual gestures at the same time is challenging [ 24 , 40 ]. Researchers in this field must create a reasonably sized database from scratch to implement and examine their sign language recognition system.…”

Section: Methodsmentioning

confidence: 99%

“…This interaction between the smart glove and computer is a clear example of human–computer interaction. There are two types of sensor-based methods used in sign language acquisition: sensors that can only detect finger bending and sensors that detect hand motion and orientation [ 23 , 24 ]. For more detailed information, we suggest reading the comprehensive survey paper “Systems-based sensory gloves for sign language recognition” [ 23 ].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Technology to Recognize American Sign Language Alphabet

Alsharif,

Altaher,

Altaher

et al. 2023

Sensors

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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 aimed at improving the quality of life for hearing-disabled individuals. This research paper presents a comprehensive study employing five distinct deep learning models to recognize hand gestures for the American Sign Language (ASL) alphabet. The primary objective of this study was to leverage contemporary technology to bridge the communication gap between hearing-impaired individuals and individuals with no hearing impairment. The models utilized in this research include AlexNet, ConvNeXt, EfficientNet, ResNet-50, and VisionTransformer were trained and tested using an extensive dataset comprising over 87,000 images of the ASL alphabet hand gestures. Numerous experiments were conducted, involving modifications to the architectural design parameters of the models to obtain maximum recognition accuracy. The experimental results of our study revealed that ResNet-50 achieved an exceptional accuracy rate of 99.98%, the highest among all models. EfficientNet attained an accuracy rate of 99.95%, ConvNeXt achieved 99.51% accuracy, AlexNet attained 99.50% accuracy, while VisionTransformer yielded the lowest accuracy of 88.59%.

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“…This approach reported an accuracy of 99.8% on the static signs of ASL. For more details about the deep learning techniques utilized for sign language recognition, we refer to this survey [42].…”

Section: B Vision-based Techniquesmentioning

confidence: 99%

An Efficient Two-Stream Network for Isolated Sign Language Recognition Using Accumulative Video Motion

Luqman

2022

IEEE Access

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Sign language is the primary communication medium for persons with hearing impairments. This language depends mainly on hand articulations accompanied by nonmanual gestures. Recently, there has been a growing interest in sign language recognition. In this paper, we propose a trainable deep learning network for isolated sign language recognition, which can effectively capture the spatiotemporal information using a small number of signs' frames. We propose a hierarchical sign learning module that comprises three networks: dynamic motion network (DMN), accumulative motion network (AMN), and sign recognition network (SRN). Additionally, we propose a technique to extract key postures for handling the variations in the sign samples performed by different signers. The DMN stream uses these key postures to learn the spatiotemporal information pertaining to the signs. We also propose a novel technique to represent the statical and dynamic information of sign gestures into a single frame. This approach preserves the spatial and temporal information of the sign by fusing the sign's key postures in the forward and backward directions to generate an accumulative video motion frame. This frame was used as an input to the AMN stream, and the extracted features were fused with the DMN features to be fed into the SRN for the learning and classification of signs. The proposed approach is efficient for isolated sign language recognition, especially for recognizing static signs. We evaluated this approach on the KArSL-190 and KArSL-502 Arabic sign language datasets, and the obtained results on KArSL-190 outperformed other techniques by 15% in the signer-independent mode. Additionally, the proposed approach outperformed the state-of-the-art techniques on the Argentinian sign language dataset LSA64. The code is available at https://github.com/Hamzah-Luqman/SLR_AMN.

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A comprehensive survey and taxonomy of sign language research

Cited by 30 publications

References 146 publications

A systematic review of machine learning techniques for stance detection and its applications

A systematic review of machine learning techniques for stance detection and its applications

Deep Learning Technology to Recognize American Sign Language Alphabet

An Efficient Two-Stream Network for Isolated Sign Language Recognition Using Accumulative Video Motion

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