Recent progress in sign language recognition: a review

Wali, Aamir; Shariq, Roha; Shoaib, Sajdah; Amir, Sukhan; Farhan, Asma Ahmad

doi:10.1007/s00138-023-01479-y

Cited by 5 publications

(3 citation statements)

References 38 publications

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“…Automatic gesture recognition is very engaging since hand gestures are spatiotemporally changing. The primary difficulty in sign language recognition is finding a model that can grasp the language and scale up to significant terms [2,3]. It is possible to achieve gesture recognition in one of two ways [4]: either by wearing sensual gloves on one's hands or with the aid of computer vision.…”

Section: Introductionmentioning

confidence: 99%

“…Building a hand gesture detection and recognition system involves several main steps. A high-level overview is provided as follows (see Figure 1) [1][2][3][4]: (1) Data Collection: Gather a diverse dataset of hand gesture images. These should cover different hand poses, lighting conditions, backgrounds, and skin tones.…”

Section: Introductionmentioning

confidence: 99%

“…These should cover different hand poses, lighting conditions, backgrounds, and skin tones. (2) Preprocessing: Preprocess the collected data to improve their quality and suitability for training. This may involve tasks such as resizing, normalization, noise reduction, and background subtraction.…”

Section: Introductionmentioning

confidence: 99%

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Cognitive Classifier of Hand Gesture Images for Automated Sign Language Recognition: Soft Robot Assistance Based on Neutrosophic Markov Chain Paradigm

Al-Saidi,

Ballagi,

Hassen

et al. 2024

Computers

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In recent years, Sign Language Recognition (SLR) has become an additional topic of discussion in the human–computer interface (HCI) field. The most significant difficulty confronting SLR recognition is finding algorithms that will scale effectively with a growing vocabulary size and a limited supply of training data for signer-independent applications. Due to its sensitivity to shape information, automated SLR based on hidden Markov models (HMMs) cannot characterize the confusing distributions of the observations in gesture features with sufficiently precise parameters. In order to simulate uncertainty in hypothesis spaces, many scholars provide an extension of the HMMs, utilizing higher-order fuzzy sets to generate interval-type-2 fuzzy HMMs. This expansion is helpful because it brings the uncertainty and fuzziness of conventional HMM mapping under control. The neutrosophic sets are used in this work to deal with indeterminacy in a practical SLR setting. Existing interval-type-2 fuzzy HMMs cannot consider uncertain information that includes indeterminacy. However, the neutrosophic hidden Markov model successfully identifies the best route between states when there is vagueness. This expansion is helpful because it brings the uncertainty and fuzziness of conventional HMM mapping under control. The neutrosophic three membership functions (truth, indeterminate, and falsity grades) provide more layers of autonomy for assessing HMM’s uncertainty. This approach could be helpful for an extensive vocabulary and hence seeks to solve the scalability issue. In addition, it may function independently of the signer, without needing data gloves or any other input devices. The experimental results demonstrate that the neutrosophic HMM is nearly as computationally difficult as the fuzzy HMM but has a similar performance and is more robust to gesture variations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cognitive Classifier of Hand Gesture Images for Automated Sign Language Recognition: Soft Robot Assistance Based on Neutrosophic Markov Chain Paradigm

Al-Saidi,

Ballagi,

Hassen

et al. 2024

Computers

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A Comparative Study of Globally Popular Sign Language Recognition System

Goda,

Deshani,

Kumbharana

2024

Lecture Notes in Networks and Systems

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A real-time platform for Spanish Sign Language interpretation

Morillas-Espejo,

Martinez-Martin

2024

Neural Comput & Applic

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More than 13% of the Spanish population suffers from hearing impairment. Despite the existence of several hearing aids and implants making sounds clearer and louder, there are some people unable to use them and, consequently, their only means of communication is Sign Language. However, this language is not widely known in the society. Consequently, those who are deaf or hard of hearing may be socially excluded and experience frustration as a result of the lack of communication. In this context, Sign Language Recognition and interpretation would help to break down the existing communication barriers and facilitate the creation of inclusive environments. With that aim, this paper presents a real-time platform to recognize and interpret finger-spelt words in Spanish Sign Language (Lengua de Signos Española). As finger spelling implies the recognition of each signed letter, a comparative analysis of different deep learning techniques to properly recognize the Spanish Sign Language alphabet has been carried out. For that, due to the lack of Spanish Sign Language datasets, the first step was to capture and build an image dataset representing its 30 letters. As there are static and in-motion letters, spatial and temporal analysis has been conducted by considering different kind of neural networks (Convolutional Neural Networks, Recurrent Neural Networks, and Vision Transformers). The experimental results highlight the good performance of the studied architectures, obtaining a maximum accuracy of 79.96% on previously unseen data. Finally, a real-time platform for the recognition and interpretation of finger-spelt words in Spanish Sign Language has been implemented making the communication possible.

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Recent progress in sign language recognition: a review

Cited by 5 publications

References 38 publications

Cognitive Classifier of Hand Gesture Images for Automated Sign Language Recognition: Soft Robot Assistance Based on Neutrosophic Markov Chain Paradigm

Cognitive Classifier of Hand Gesture Images for Automated Sign Language Recognition: Soft Robot Assistance Based on Neutrosophic Markov Chain Paradigm

A Comparative Study of Globally Popular Sign Language Recognition System

A real-time platform for Spanish Sign Language interpretation

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