Signing Exact English (SEE): Modeling and recognition

Kong, Weizheng; Ranganath, S.

doi:10.1016/j.patcog.2007.10.016

Cited by 20 publications

(9 citation statements)

References 35 publications

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“…Hand movement trajectory was classified using vector quantization principal component analysis (VQPCA). The system yielded recognition accuracy up to 96.1% [ 87 ]. Moreover, a few (4/12) experimented with another commercial data glove known as the 5DT data glove to develop their SLR systems.…”

Section: The Analysis Resultsmentioning

confidence: 99%

“…Glove-based systems are important techniques that are used to obtain hand movement data [ 57 ]. Despite them having been around for more than three decades, this field of research is still extremely active [ 87 ]. It is also obvious that technological advancements in computing, materials, sensors, and processing-classification methods will contribute to making the new generation of glove systems more powerful, highly accurate, comfortable, cheap, and possible to use in many applications [ 36 , 66 ].…”

Section: Discussionmentioning

confidence: 99%

“…In terms of the physical differentiation of each apprentice, the beginner cannot accurately synchronize his/her movement with that of a proficient person [ 40 ]. As a result, even if the beginner carries out the motion correctly according to his/her convictions, the listener may still have difficulties interpreting this motion; hence, the apprentice receives a low score because of the great variance from the skilled person [ 87 ]. Differences in several angular values of the finger joint should also be observed during gesture generation.…”

Section: Discussionmentioning

confidence: 99%

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A Review on Systems-Based Sensory Gloves for Sign Language Recognition State of the Art between 2007 and 2017

Ahmed

Zaidan

et al. 2018

Sensors

187

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Loss of the ability to speak or hear exerts psychological and social impacts on the affected persons due to the lack of proper communication. Multiple and systematic scholarly interventions that vary according to context have been implemented to overcome disability-related difficulties. Sign language recognition (SLR) systems based on sensory gloves are significant innovations that aim to procure data on the shape or movement of the human hand. Innovative technology for this matter is mainly restricted and dispersed. The available trends and gaps should be explored in this research approach to provide valuable insights into technological environments. Thus, a review is conducted to create a coherent taxonomy to describe the latest research divided into four main categories: development, framework, other hand gesture recognition, and reviews and surveys. Then, we conduct analyses of the glove systems for SLR device characteristics, develop a roadmap for technology evolution, discuss its limitations, and provide valuable insights into technological environments. This will help researchers to understand the current options and gaps in this area, thus contributing to this line of research.

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Section: The Analysis Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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A Review on Systems-Based Sensory Gloves for Sign Language Recognition State of the Art between 2007 and 2017

Ahmed

Zaidan

et al. 2018

Sensors

187

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“…The criterion of data acquisition method is often used in the literature to classify SLR methodologies in two fundamental categories. The first category consists of direct measurement methods that involve the use of data gloves, multiple sensors attached to hands and body and motion capturing systems [1] [2]. Such sensors allow the extraction of accurate motion data that describe the movement of hands, head and other body parts that are useful for the classification of signs.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Approach for Analyzing Video and Skeletal Features in Sign Language Recognition

Konstantinidis

Dimitropoulos

Daras

2018

2018 IEEE International Conference on Imaging Systems and Techniques (IST)

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Sign language recognition (SLR) refers to the classification of signs with a specific meaning performed by the deaf and/or hearing-impaired people in their everyday communication. In this work, we propose a deep learning based framework, in which we examine and analyze the contribution of video (image and optical flow) and skeletal (body, hand and face) features in the challenging task of isolated SLR, in which each signed video corresponds to a single word. Moreover, we employ various fusion schemes in order to identify the optimal way to combine the information obtained from the various feature representations and propose a robust SLR methodology. Our experimentation on two sign language datasets and the comparison with state-of-the-art SLR methods reveals the superiority of optimally combining skeletal and video features for SLR tasks.

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“…Previous work on SLR can be categorized based on the data acquisition method, resulting in either direct measurement or vision-based approaches. Direct measurement methods rely on motion data acquired by data gloves, sensors or motion capturing systems [1] [2]. The extracted motion data can provide accurate tracking of hands, fingers and other body parts, leading to the development of robust SLR methodologies, at the expense of costly setups and obtrusive systems as the movements of a signer are severely restricted from wearing the input devices.…”

Section: Introductionmentioning

confidence: 99%

Sign Language Recognition Based on Hand and Body Skeletal Data

Konstantinidis

Dimitropoulos

Daras

2018

2018 - 3dtv-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3dtv-Con)

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Sign language recognition (SLR) is a challenging, but highly important research field for several computer vision systems that attempt to facilitate the communication among the deaf and hearing impaired people. In this work, we propose an accurate and robust deep learning-based methodology for sign language recognition from video sequences. Our novel method relies on hand and body skeletal features extracted from RGB videos and, therefore, it acquires highly discriminative for gesture recognition skeletal data without the need for any additional equipment, such as data gloves, that may restrict signer's movements. Experimentation on a large publicly available sign language dataset reveals the superiority of our methodology with respect to other state of the art approaches relying solely on RGB features.

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Signing Exact English (SEE): Modeling and recognition

Cited by 20 publications

References 35 publications

A Review on Systems-Based Sensory Gloves for Sign Language Recognition State of the Art between 2007 and 2017

A Review on Systems-Based Sensory Gloves for Sign Language Recognition State of the Art between 2007 and 2017

A Deep Learning Approach for Analyzing Video and Skeletal Features in Sign Language Recognition

Sign Language Recognition Based on Hand and Body Skeletal Data

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