Efficient Noise Reduction and HOG Feature Extraction for Sign Language Recognition

Mahmud, Iqbal; Tabassum, Tasnim; Uddin, Md. Palash; Ali, Emran; Nitu, Adiba Mahjabin; Afjal, Masud Ibn

doi:10.1109/icaeee.2018.8642983

Cited by 13 publications

(5 citation statements)

References 5 publications

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“…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]. To realize sign language recognition for non-continuous and non-time-series data, there are some works such as the method of k-NN [19], similarity calculation using Euclidean distance [20], cosine similarity [19][21], ANN [22], SVM [23], and convolutional neural network (CNN) [24]. Reference [25] provides a research survey on recognizing emotions from body gestures.…”

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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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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“…These processes aimed to extract features from static images or simple video clips of the relevant signs. SLR was performed using various traditional classification algorithms based on features extracted from sign language letters, numbers, or words using feature extraction techniques such as Hough Transform [11], Contour Analysis [12], Local Binary Pattern [13,14], Gabor Filter [15], HOG [16,17], SIFT [18,19], SURF [20], optical flow [21], Dynamic Time Warping [22], Hidden Markov Models [23,24], and temporal accumulative features [25]. Methods based on handcrafted features and traditional machine learning techniques, while effective for a predefined and limited number of signs, typically struggle to recognize new or rare signs due to their limited flexibility and generalization ability.…”

Section: Related Workmentioning

confidence: 99%

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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“…3, the main flow of HOG algorithm implementation was provided. Mahmud et al [21] employed HOG to feature extraction and utilized k-Nearest Neighbor (KNN) to classify American sign language. This method provides superior accuracy (94.23%) to the compared approach (86%).…”

Section: Histogram Of Oriented Gradientsmentioning

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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Efficient Noise Reduction and HOG Feature Extraction for Sign Language Recognition

Cited by 13 publications

References 5 publications

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

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

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

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

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