Leap Motion based Myanmar Sign Language Recognition using Machine Learning

Zaw, Hein; Htoo, Thet Paing; Aye, Bawin; Htet, Sai Myo

doi:10.1109/elconrus51938.2021.9396496

Cited by 16 publications

(4 citation statements)

References 4 publications

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“…Using a vision-based approach we can reduce the cost by only the trained dataset and model to recognize the sign with only captured devices such as a webcam or by using a droidcam (a third-party mobile camera to a webcam app) to capture from a device. Used method(s) Accuracy (%) [13] Cyber gloves and HMMs 95 [16] Leap motion devices 90.82 [20] Flex sensors 90.34 (precision score) [21] Data gloves and 3D position trackers 91.9 [22] One-handed glove-based system and HMMs 94 LSTM 94.3 Proposed work GRU 76 CNN 89.07…”

Section: Resultsmentioning

confidence: 99%

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An Adam based CNN and LSTM approach for sign language recognition in real time for deaf people

Kumer Paul,

Ala Walid,

Rani Paul

et al. 2024

Bulletin EEI

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Hand gestures and sign language are crucial modes of communication for deaf individuals. Since most people can't understand sign language, it's hard for a mute and an average person to talk to each other. Because of technological progress, computer vision and deep learning can now be used to count. This paper shows two ways to use deep knowledge to recognize sign language. These methods help regular people understand sign language and improve their communication. Based on American sign language (ASL), two separate datasets have been constructed; the first has 26 signs, and the other contains three significant symbols with the crucial sequence of frames or videos for regular communication. This study looks at three different models: the improved ResNet-based convolutional neural network (CNN), the long short-term memory (LSTM), and the gated recurrent unit (GRU). The first dataset is used to fit and assess the CNN model. With the adaptive moment estimation (Adam) optimizer, CNN obtains an accuracy of 89.07%. In contrast, the second dataset is given to LSTM and GRU and a comparison has been conducted. LSTM does better than GRU in all classes. LSTM has a 94.3% accuracy, while GRU only manages 79.3%. Our preliminary models' real-time performance is also highlighted.

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Section: Resultsmentioning

confidence: 99%

“…Hein et al [16] implemented their way with two segments: a training segment and a classification segment. First, a webcam input video was taken for the training part.…”

Section: Literature Reviewmentioning

confidence: 99%

An Adam based CNN and LSTM approach for sign language recognition in real time for deaf people

Kumer Paul,

Ala Walid,

Rani Paul

et al. 2024

Bulletin EEI

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show abstract

“…By altering the dataset, the system can be used for several sign languages. Zaw Hein, Thet Paing Htoo, Bawin Aye, Sai Myo Htet, Kyaw Zaw Ye, et al [15], suggested Language Recognition in two sections: a section for categorization and a section for training. First, we used a camera to record the input video for the training section.…”

Section: Related Workmentioning

confidence: 99%

A Survey on Advancing Sign Language Recognition Using Machine Learning

2023

IRJMETS

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The primary means of communication between humans is voice and language. We are able to understand each other's thoughts because we have hearing. These days, speech recognition technology even allows us to give commands. What about someone who gradually loses their ability to hear and speak? Therefore, sign language is the primary means of communication for deaf and hard of hearing individuals. Additionally, substantial research is being done in the area of automatic sign language interpretation to ensure that these individuals can lead independent lives. In this field, numerous methods and algorithms have been developed through the application of artificial intelligence and image processing. All sign language identification systems are taught to identify the signs and converting them into required pattern. In this paper we explore existing work where Sign Language is captured as a series of images and it's processed with the help of Python and then it's converted to speech and text.

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“…Subsequent detection of the IF signal allows the extraction of motion information for the target. Because of its small antenna size and contactless operation capability, FMCW radar finds applications in various scenarios, including autonomous driving [8], sign language recognition [9][10][11][12], home automation [13,14], and many other fields. Therefore, FMCW radar plays a crucial role in human-machine interaction.…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised FMCW Radar Hand Gesture Recognition via Pseudo-Label Consistency Learning

Shi,

Qiao,

Shu

et al. 2024

Remote Sensing

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Hand gesture recognition is pivotal in facilitating human–machine interaction within the Internet of Things. Nevertheless, it encounters challenges, including labeling expenses and robustness. To tackle these issues, we propose a semi-supervised learning framework guided by pseudo-label consistency. This framework utilizes a dual-branch structure with a mean-teacher network. Within this setup, a global and locally guided self-supervised learning encoder acts as a feature extractor in a teacher–student network to efficiently extract features, maximizing data utilization to enhance feature representation. Additionally, we introduce a pseudo-label Consistency-Guided Mean-Teacher model, where simulated noise is incorporated to generate newly unlabeled samples for the teacher model before advancing to the subsequent stage. By enforcing consistency constraints between the outputs of the teacher and student models, we alleviate accuracy degradation resulting from individual differences and interference from other body parts, thereby bolstering the network’s robustness. Ultimately, the teacher model undergoes refinement through exponential moving averages to achieve stable weights. We evaluate our semi-supervised method on two publicly available hand gesture datasets and compare it with several state-of-the-art fully-supervised algorithms. The results demonstrate the robustness of our method, achieving an accuracy rate exceeding 99% across both datasets.

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Leap Motion based Myanmar Sign Language Recognition using Machine Learning

Cited by 16 publications

References 4 publications

An Adam based CNN and LSTM approach for sign language recognition in real time for deaf people

An Adam based CNN and LSTM approach for sign language recognition in real time for deaf people

A Survey on Advancing Sign Language Recognition Using Machine Learning

Semi-Supervised FMCW Radar Hand Gesture Recognition via Pseudo-Label Consistency Learning

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