Chinese Sign Language Alphabet Recognition Based on Random Forest Algorithm

Yuan, Simin; Wang, Yuan; Wang, Xin; Deng, Hanjie; Sun, Shangqi; Huī, Wánɡ; Huang, Pingao; Li, Guanglin

doi:10.1109/metroind4.0iot48571.2020.9138285

Cited by 14 publications

(5 citation statements)

References 14 publications

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“…However, it was mentioned that ANN and SVM classifiers may not always provide the best precision. The article published by the authors of [21] claims that the random forest algorithm outperforms the ANN and SVM classifiers in classifying most of the gestures. Additionally, research conducted by the authors of [94] demonstrated that the KNN classifier achieves better recognition results compared to the SVMs.…”

Section: Discussionmentioning

confidence: 99%

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Sign Language Recognition Using the Electromyographic Signal: A Systematic Literature Review

Ben Haj Amor,

El Ghoul,

Jemni

2023

Sensors

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The analysis and recognition of sign languages are currently active fields of research focused on sign recognition. Various approaches differ in terms of analysis methods and the devices used for sign acquisition. Traditional methods rely on video analysis or spatial positioning data calculated using motion capture tools. In contrast to these conventional recognition and classification approaches, electromyogram (EMG) signals, which measure muscle electrical activity, offer potential technology for detecting gestures. These EMG-based approaches have recently gained attention due to their advantages. This prompted us to conduct a comprehensive study on the methods, approaches, and projects utilizing EMG sensors for sign language handshape recognition. In this paper, we provided an overview of the sign language recognition field through a literature review, with the objective of offering an in-depth review of the most significant techniques. These techniques were categorized in this article based on their respective methodologies. The survey discussed the progress and challenges in sign language recognition systems based on surface electromyography (sEMG) signals. These systems have shown promise but face issues like sEMG data variability and sensor placement. Multiple sensors enhance reliability and accuracy. Machine learning, including deep learning, is used to address these challenges. Common classifiers in sEMG-based sign language recognition include SVM, ANN, CNN, KNN, HMM, and LSTM. While SVM and ANN are widely used, random forest and KNN have shown better performance in some cases. A multilayer perceptron neural network achieved perfect accuracy in one study. CNN, often paired with LSTM, ranks as the third most popular classifier and can achieve exceptional accuracy, reaching up to 99.6% when utilizing both EMG and IMU data. LSTM is highly regarded for handling sequential dependencies in EMG signals, making it a critical component of sign language recognition systems. In summary, the survey highlights the prevalence of SVM and ANN classifiers but also suggests the effectiveness of alternative classifiers like random forests and KNNs. LSTM emerges as the most suitable algorithm for capturing sequential dependencies and improving gesture recognition in EMG-based sign language recognition systems.

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Section: Discussionmentioning

confidence: 99%

“…In a study by Simin Yuan et al [21], sEMG signals were utilized to recognize 30 Chinese sign language alphabet letters. The Delsys Trigno division, which contains eight sEMG sensors placed on the forearm, was employed for data collection from four hearing subjects.…”

Section: Other Proposed Approachesmentioning

confidence: 99%

Sign Language Recognition Using the Electromyographic Signal: A Systematic Literature Review

Ben Haj Amor,

El Ghoul,

Jemni

2023

Sensors

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“…According to reference [3], sign language recognition methods can be divided into two categories: continuous recognition of multiple sign words and discontinuous recognition. To realize continuous recognition, there are some works such as the hidden Markov model (HMM) and dynamic time warping (DTW) [14], and methods using Random Forest, artificial neural networks (ANN), and support vector machines (SVM) [15]. To realize non-continuous recognition, there are some studies, such as the k-nearest neighbor (k-NN) method [16], SVM [17], and sparse Bayesian classification of feature vectors generated from motion gradient orientation images extracted from input videos [18].…”

Section: Related Workmentioning

confidence: 99%

A New Global Sign Language Recognition System Utilizing the Editable Mediator: Integration with Local Hand Shape Recognition

Nakanishi,

Minematsu,

Okada

et al. 2024

Frontiers in Artificial Intelligence and Applications

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We introduced a novel approach to global sign language recognition by leveraging the capabilities of the Editable Mediator. Traditional methods have often been limited to recognizing sign languages from specific linguistic regions, necessitating ad hoc implementation for multilingual regions. Our method aims to bridge this gap by providing a unified framework for recognizing sign languages in various linguistic areas and promoting global communication. At the core of our system is the Editable Mediator, a mechanism that determines the actual sign meaning from various local hand-shape recognitions. Instead of focusing on specific sign language notations, such as HamNoSys, our approach emphasizes the recognition of common primitive actions shared across different sign languages. These primitive actions are recognized by multiple modules, and their combinations are interpreted by the Editable Mediator to determine the intended sign-language message. This architecture not only simplifies the recognition process, but also offers flexibility. By merely editing the Editable Mediator, our system can adapt to various sign languages worldwide without the need for extensive retraining or ad hoc implementation. This innovation reduces barriers to introducing new sign language systems and promotes a more inclusive global communication platform.

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“…7. Yuan et al [44] employed sEMG and an RF algorithm to identify 30 alphabets, achieving an average accuracy of 95.48%. Su et al [45] utilized the RF algorithm to implement SLR systems based on ACC-sEMG.…”

Section: Random Forestmentioning

confidence: 99%

A Survey on Chinese Sign Language Recognition: From Traditional Methods to Artificial Intelligence

Jiang,

Zhang,

Lei

et al. 2024

CMES

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Research on Chinese Sign Language (CSL) provides convenience and support for individuals with hearing impairments to communicate and integrate into society. This article reviews the relevant literature on Chinese Sign Language Recognition (CSLR) in the past 20 years. Hidden Markov Models (HMM), Support Vector Machines (SVM), and Dynamic Time Warping (DTW) were found to be the most commonly employed technologies among traditional identification methods. Benefiting from the rapid development of computer vision and artificial intelligence technology, Convolutional Neural Networks (CNN), 3D-CNN, YOLO, Capsule Network (CapsNet) and various deep neural networks have sprung up. Deep Neural Networks (DNNs) and their derived models are integral to modern artificial intelligence recognition methods. In addition, technologies that were widely used in the early days have also been integrated and applied to specific hybrid models and customized identification methods. Sign language data collection includes acquiring data from data gloves, data sensors (such as Kinect, Leap Motion, etc.), and high-definition photography. Meanwhile, facial expression recognition, complex background processing, and 3D sign language recognition have also attracted research interests among scholars. Due to the uniqueness and complexity of Chinese sign language, accuracy, robustness, real-time performance, and user independence are significant challenges for future sign language recognition research. Additionally, suitable datasets and evaluation criteria are also worth pursuing.

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Chinese Sign Language Alphabet Recognition Based on Random Forest Algorithm

Cited by 14 publications

References 14 publications

Sign Language Recognition Using the Electromyographic Signal: A Systematic Literature Review

Sign Language Recognition Using the Electromyographic Signal: A Systematic Literature Review

A New Global Sign Language Recognition System Utilizing the Editable Mediator: Integration with Local Hand Shape Recognition

A Survey on Chinese Sign Language Recognition: From Traditional Methods to Artificial Intelligence

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