Unraveling a Decade: A Comprehensive Survey on Isolated Sign Language Recognition

Sarhan, Noha; Frintrop, Simone

doi:10.1109/iccvw60793.2023.00345

2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) 2023

DOI: 10.1109/iccvw60793.2023.00345

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Unraveling a Decade: A Comprehensive Survey on Isolated Sign Language Recognition

Noha Sarhan,

Simone Frintrop

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2024

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Cited by 4 publications

References 51 publications

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MASA: Motion-Aware Masked Autoencoder With Semantic Alignment for Sign Language Recognition

Zhao,

Hu,

Zhou

et al. 2024

IEEE Trans. Circuits Syst. Video Technol.

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MASA: Motion-Aware Masked Autoencoder With Semantic Alignment for Sign Language Recognition

Zhao,

Hu,

Zhou

et al. 2024

IEEE Trans. Circuits Syst. Video Technol.

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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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Multi-Stream Isolated Sign Language Recognition Based on Finger Features Derived from Pose Data

Akdag,

Baykan

2024

Electronics

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This study introduces an innovative multichannel approach that focuses on the features and configurations of fingers in isolated sign language recognition. The foundation of this approach is based on three different types of data, derived from finger pose data obtained using MediaPipe and processed in separate channels. Using these multichannel data, we trained the proposed MultiChannel-MobileNetV2 model to provide a detailed analysis of finger movements. In our study, we first subject the features extracted from all trained models to dimensionality reduction using Principal Component Analysis. Subsequently, we combine these processed features for classification using a Support Vector Machine. Furthermore, our proposed method includes processing body and facial information using MobileNetV2. Our final proposed sign language recognition method has achieved remarkable accuracy rates of 97.15%, 95.13%, 99.78%, and 95.37% on the BosphorusSign22k-general, BosphorusSign22k, LSA64, and GSL datasets, respectively. These results underscore the generalizability and adaptability of the proposed method, proving its competitive edge over existing studies in the literature.

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Unraveling a Decade: A Comprehensive Survey on Isolated Sign Language Recognition

Cited by 4 publications

References 51 publications

MASA: Motion-Aware Masked Autoencoder With Semantic Alignment for Sign Language Recognition

MASA: Motion-Aware Masked Autoencoder With Semantic Alignment for Sign Language Recognition

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

Multi-Stream Isolated Sign Language Recognition Based on Finger Features Derived from Pose Data

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