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Author: Yohanes Satria Nugroho
Yohanes Satria Nugroho
Thesis Title: Lightweight American Sign Language Recognition Using a Deep Learning Approach
Lightweight American Sign Language Recognition Using a Deep Learning Approach
Advisor: 楊傳凱
Chuan-Kai Yang
Committee: 賴源正
Yuan-Cheng Lai
林伯慎
Bor-Shen Lin
Degree: 碩士
Master
Department: 管理學院 - 資訊管理系
Department of Information Management
Thesis Publication Year: 2023
Graduation Academic Year: 111
Language: 英文
Pages: 54
Keywords (in Chinese): Sign Language RecognitionLightweight ModelKeypoints Estimation
Keywords (in other languages): Sign Language Recognition, Lightweight Model, Keypoints Estimation
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Sign Language Recognition is a variant of the Action Recognition that consists of more detailed features, such as hand shapes and movements. Researchers have been trying to apply computer-based methods to tackle this task throughout the years. However, the methods proposed are constrained by hardware limitations thus limiting them from being applied in a real-life situations.

In this research, we explore the possibilities of creating a lightweight Sign Language Recognition model so that it can be applied in real-life situations. We explore two different approaches. First we extract keypoints and use a simple LSTM model to do the recognition and get 75\% of Top-1 Validation Accuracy. For the second one, we used the lightweight MoViNet A0 model and achieved 71\% of Top-1 Test accuracy. Although these models achieved a little bit worse result compared to the state-of-the-art I3D, the complexity in terms of FLOPs are far more better.


Sign Language Recognition is a variant of the Action Recognition that consists of more detailed features, such as hand shapes and movements. Researchers have been trying to apply computer-based methods to tackle this task throughout the years. However, the methods proposed are constrained by hardware limitations thus limiting them from being applied in a real-life situations.

In this research, we explore the possibilities of creating a lightweight Sign Language Recognition model so that it can be applied in real-life situations. We explore two different approaches. First we extract keypoints and use a simple LSTM model to do the recognition and get 75\% of Top-1 Validation Accuracy. For the second one, we used the lightweight MoViNet A0 model and achieved 71\% of Top-1 Test accuracy. Although these models achieved a little bit worse result compared to the state-of-the-art I3D, the complexity in terms of FLOPs are far more better.

Master’s Thesis Recommendation Form . . . . . . . . . . . . . . . i Qualification Form by Master’s Degree Examination Committee . . ii Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . iv Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Research Outline . . . . . . . . . . . . . . . . . . . . . . 4 2 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1 Action Recognition . . . . . . . . . . . . . . . . . . . . . 5 2.2 Sign Language Recognition . . . . . . . . . . . . . . . . . 7 2.3 Human Pose Estimation . . . . . . . . . . . . . . . . . . . 8 2.4 MoViNets . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3 Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.1 Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.1.1 Keypoints + LSTM . . . . . . . . . . . . . . . . . 12 3.1.2 MoViNet . . . . . . . . . . . . . . . . . . . . . . 15 3.2 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.3 Dataset Processing . . . . . . . . . . . . . . . . . . . . . 19 3.3.1 Keypoints Input . . . . . . . . . . . . . . . . . . . 20 3.3.2 RGB Input . . . . . . . . . . . . . . . . . . . . . 23 4 Experiments & Results . . . . . . . . . . . . . . . . . . . . . . 24 4.1 Training . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.1.1 LSTM . . . . . . . . . . . . . . . . . . . . . . . . 25 4.1.2 MoViNet . . . . . . . . . . . . . . . . . . . . . . 27 4.2 Experimental Results . . . . . . . . . . . . . . . . . . . . 30 4.3 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . 36 5 Conclusion and Discussion . . . . . . . . . . . . . . . . . . . . 39 5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . 39 5.2 Future Works . . . . . . . . . . . . . . . . . . . . . . . . 40 Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 vi

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