Deep Learning for American Sign Language Fingerspelling Recognition System

Nguyen, Huy B.D; Ngoc, Hung

doi:10.1109/ict.2019.8798856

Cited by 32 publications

(29 citation statements)

References 13 publications

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“…Using still photos or continuous recordings in RGB format has the advantage of good resolution, but depth imaging does a better job at determining how far an item might be located from a fixed point. There are certain algorithms that use both types of visual data in combination [72]. Thermal imaging is also an intriguing possibility, even if it is used more rarely than the previous two formats.…”

Section: ) Hybridmentioning

confidence: 99%

“…In [71], a convolution layer was used to extract various features of the input. The authors in [72] used a trained CNN as the feature extractor for an SVM.…”

Section: ) Feature Extractionmentioning

confidence: 99%

“…Past experiments have used SVM for this and many other purposes. Nguyen et al [72] applied multiclass SVMs to learn extracted data . The validation accuracy of the CNN-SVM model was lower than that of the HOG-LBP-SVM model.…”

Section: ) Machine Learningmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning for Sign Language Recognition: Current Techniques, Benchmarks, and Open Issues

Al‐Qurishi¹,

Khalid²,

Souissi³

2021

IEEE Access

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People with hearing impairments are found worldwide; therefore, the development of effective local level sign language recognition (SLR) tools is essential. We conducted a comprehensive review of automated sign language recognition based on machine/deep learning methods and techniques published between 2014 and 2021 and concluded that the current methods require conceptual classification to interpret all available data correctly. Thus, we turned our attention to elements that are common to almost all sign language recognition methodologies. This paper discusses their relative strengths and weaknesses, and we propose a general framework for researchers. This study also indicates that input modalities bear great significance in this field; it appears that recognition based on a combination of data sources, including vision-based and sensor-based channels, is superior to a unimodal analysis. In addition, recent advances have allowed researchers to move from simple recognition of sign language characters and words towards the capacity to translate continuous sign language communication with minimal delay. Many of the presented models are relatively effective for a range of tasks, but none currently possess the necessary generalization potential for commercial deployment. However, the pace of research is encouraging, and further progress is expected if specific difficulties are resolved.

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Section: ) Hybridmentioning

confidence: 99%

“…In [71], a convolution layer was used to extract various features of the input. The authors in [72] used a trained CNN as the feature extractor for an SVM.…”

Section: ) Feature Extractionmentioning

confidence: 99%

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Deep Learning for Sign Language Recognition: Current Techniques, Benchmarks, and Open Issues

Al‐Qurishi¹,

Khalid²,

Souissi³

2021

IEEE Access

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“…Alternatively, vision-based systems don't rely on such sensors, rather rely on the inputs given by the camera. Such systems mostly use convolutional neural networks for classifying hand gestures [4][5][6][7]. Pardasani et al used a convolutional neural network that is similar to LeNet5 [9] for classifying hand gestures [6].…”

Section: Related Workmentioning

confidence: 99%

“…There are primarily two different methods for recognising hand gestures, the first one relies on sensor data from gloves such as flex sensor [2,3] and the other relies on vision-based methods that rely on the data captured from the camera and processes it to extract valuable information [4][5][6][7]. Vision-based methods are more popular and they mostly use Convolutional Neural Networks as they are known to have excellent classification abilities and are often used for this purpose.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Sign Language Interpreter on Embedded Platform

Mulchandani

Paunwala

2021

Communications in Computer and Information Science

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Sign language is the primary way of communication for speechimpaired people. American Sign Language is one of the most popular sign languages and is used by over 250,000 people around the world. However, this language is only decipherable by the people who have the same disabilities, and it is not understood by masses. Hence, a need arises for an interpreter that can convert this language to text and speech. The existing research focuses on effectively converting sign language to text and speech; however, less emphasis is laid on building a system that is not only accurate but efficient as well which could be run on a compact embedded platform. This is necessary so that a portable interpreter can be made which can be easily carried and used by such people for their convenience making their day to day life easier. In this research, we propose an implementation based on MobileNet architecture, that is not only accurate but efficient as well. Moreover; a demonstration of the entire framework has also been shown on Jetson Nano.

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A review on computational methods based automated sign language recognition system for hearing and speech impaired community

Robert

Hemanth

2023

Concurrency and Computation

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Summary The recent advancements in computer vision and deep learning have led to promising progress in various motion detection and gesture recognition methods. Thriving efforts in the field of sign language recognition (SLR) during recent years led to interaction between humans and computer systems. Contributing a real‐time automated Sign Language recognition system will be a remarkable treasure for hearing and speech‐impaired people that will break the barriers of interaction with the real world. Albeit several research works are accomplished in sign language recognition, there is still a demand for developing a real‐time automated sign language recognition system. Compared to other methodologies, the techniques adopted may have advantages and disadvantages, and they vary from researcher to researcher. There are still some issues with employing these SLR models and procedures regularly, even though numerous research studies have been conducted to determine the most acceptable methods and models for sign language recognition. It gets more expensive and difficult in terms of resources when the developed automated SLR system becomes available as a product. Hence, for the welfare of the hearing and speech‐impaired community, the researchers are still endeavoring way to find a cost‐effective method. This work brings forth the challenges faced by scientists in developing a cost‐effective commercial prototype for the hearing and speech‐impaired community. This paper also explores and analyses various deep learning techniques and methods used in developing a sign language recognition system. The objective behind this work is to identify the best method which produces high accuracy in developing a cost‐effective sign language recognition system aiding communication between signers and non‐signers so that they can be a part of growing technology.

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Deep Learning for American Sign Language Fingerspelling Recognition System

Cited by 32 publications

References 13 publications

Deep Learning for Sign Language Recognition: Current Techniques, Benchmarks, and Open Issues

Deep Learning for Sign Language Recognition: Current Techniques, Benchmarks, and Open Issues

Real-Time Sign Language Interpreter on Embedded Platform

A review on computational methods based automated sign language recognition system for hearing and speech impaired community

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