Feature Extraction Technique for Vision-Based Indian Sign Language Recognition System: A Review

Tyagi, Akansha; Bansal, Sandhya

doi:10.1007/978-981-15-6876-3_4

Cited by 21 publications

(5 citation statements)

References 56 publications

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“…One of several projected works [23] also included a dynamic SLR system for ISL. A grid-based system was presented employing ISL [24] for real-time hand position and sign recognition. An alternative Isolated Sign Language Recognition (ISLR) with Bayesian KNN was deployed for eliminating sign redundancies [15].…”

Section: Related Workmentioning

confidence: 99%

Multi-Semantic Discriminative Feature Learning for Sign Gesture Recognition Using Hybrid Deep Neural Architecture

et al. 2023

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The speech and hearing-impaired community use sign language as the primary means of communication. It is quite challenging for the general population to interpret or learn sign language completely. A sign language recognition system must be designed and developed to address this communication barrier. Most current sign language recognition systems rely on wearable sensors, keeping the recognition system unaffordable for most individuals. Moreover, the existing vision-based sign recognition frameworks do not consider all of the spatial and temporal information required for accurate recognition. A novel vison-based hybrid deep neural net methodology is proposed in this study for recognizing Indian and Russian sign gestures. The proposed framework is aimed to establish a single framework for tracking and extracting multisemantic properties, such as non-manual components and manual co-articulations. Furthermore, spatial feature extraction from the sign gestures is deployed using a 3D deep neural net with atrous convolutions. The temporal and sequential feature extraction is carried out by employing attention-based Bi-LSTM. In addition, the distinguished abstract feature extraction is done using the modified autoencoders. The discriminative feature extraction for differentiating the sign gestures from unwanted transition gestures is done by leveraging the hybrid attention module. The experimentation of the proposed model has been carried out on the novel multi-signer Indo-Russian sign language dataset. The proposed sign language recognition framework with hybrid neural net yields better results than other state-of-the-art frameworks.

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Section: Related Workmentioning

confidence: 99%

Multi-Semantic Discriminative Feature Learning for Sign Gesture Recognition Using Hybrid Deep Neural Architecture

et al. 2023

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“…In addition, referring to sign languages as 'gestures', 'mimicry' or 'communication tools', or being 'specifically developed for [deaf] people' (as in, e.g. [6,8,45,87]; and many others) are inaccurate and offensive ways to talk about natural human languages. Börstell [9] has shown that this problem of ableist language use when referring to sign languages and deaf communities is far more prevalent in the field of technology than other fields like linguistics, education, and health-reflecting low levels of deaf awareness and deaf involvement in SLT research.…”

Section: Learn About Sign Languages and Deaf Peoplementioning

confidence: 99%

Best practices for sign language technology research

Fox,

Woll,

Cormier

2023

Univ Access Inf Soc

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Research on sign language technology (SLT) has steadily increased in recent decades, and yet, common mistakes and pitfalls have significantly hindered progress in the field. The purpose of this paper is to examine some of the most prominent issues and suggest practical steps to overcome them, outlining the best practices to consider when conducting SLT research. These practices cluster around the five following issues: (1) knowledge of the specific sign language at the centre of the research and of sign languages more generally; (2) involving deaf people at the centre of research, including researchers who are themselves deaf; (3) motivations of the researcher and the relationship to the views of the sign language community; (4) what sign language data needs to be considered; (5) full recognition of the challenges posed by such research.

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“…Additional techniques include edge detection, which identifies the boundaries of objects within an image, aiding in segmenting different individuals within a crowd [16].  Feature extraction and representation: complements this by identifying specific 'features' or 'attributes' within the data, such as facial features or body shapes, helping distinguish different individuals [17], [18]. Feature representation translates these attributes into a format that computer systems can understand and process [19].…”

Section: Vision-basedmentioning

confidence: 99%

Advancements in Crowd-Monitoring System: A Comprehensive Analysis of Systematic Approaches and Automation Algorithms: State-of-the-Art

Ameen¹,

Stone²

2023

IJNSA

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Growing apprehensions surrounding public safety have captured the attention of numerous governments and security agencies across the globe. These entities are increasingly acknowledging the imperative need for reliable and secure crowd-monitoring systems to address these concerns. Effectively managing human gatherings necessitates proactive measures to prevent unforeseen events or complications, ensuring a safe and well-coordinated environment. The scarcity of research focusing on crowd monitoring systems and their security implications has given rise to a burgeoning area of investigation, exploring potential approaches to safeguard human congregations effectively. Crowd monitoring systems depend on a bifurcated approach, encompassing vision-based and non-vision-based technologies. An in-depth analysis of these two methodologies will be conducted in this research. The efficacy of these approaches is contingent upon the specific environment and temporal context in which they are deployed, as they each offer distinct advantages. This paper endeavors to present an in-depth analysis of the recent incorporation of artificial intelligence (AI) algorithms and models into automated systems, emphasizing their contemporary applications and effectiveness in various contexts.

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Feature Extraction Technique for Vision-Based Indian Sign Language Recognition System: A Review

Cited by 21 publications

References 56 publications

Multi-Semantic Discriminative Feature Learning for Sign Gesture Recognition Using Hybrid Deep Neural Architecture

Multi-Semantic Discriminative Feature Learning for Sign Gesture Recognition Using Hybrid Deep Neural Architecture

Best practices for sign language technology research

Advancements in Crowd-Monitoring System: A Comprehensive Analysis of Systematic Approaches and Automation Algorithms: State-of-the-Art

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