COHST and Wavelet Features Based Static ASL Numbers Recognition

Thalange, Asha; Dixit, Shantanu K.

doi:10.1016/j.procs.2016.07.367

Cited by 16 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, despite the high specification camera, which most smartphones possess [ 18 ], there are various problems such as the limited field of view of the capturing device, high computational costs [ 24 , 25 ], and the need for multiple cameras to obtain robust results (due to problems of depth and occlusion [ 26 , 27 ]); these issues are inherent to this system and render the entire system futile for the development of real-time recognition applications. In [ 28 ], two new feature extraction techniques of Combined Orientation Histogram and Statistical (COHST) Features and Wavelet Features are presented for the recognition of static signs of numbers 0 to 9, of American Sign Language (ASL). System performance is measured by extracting four different features—Orientation Histogram, Statistical Measures, COHST Features, and Wavelet Features—using a neural network.…”

Section: Introductionmentioning

confidence: 99%

A Review on Systems-Based Sensory Gloves for Sign Language Recognition State of the Art between 2007 and 2017

Ahmed

Zaidan

et al. 2018

Sensors

188

View full text Add to dashboard Cite

Loss of the ability to speak or hear exerts psychological and social impacts on the affected persons due to the lack of proper communication. Multiple and systematic scholarly interventions that vary according to context have been implemented to overcome disability-related difficulties. Sign language recognition (SLR) systems based on sensory gloves are significant innovations that aim to procure data on the shape or movement of the human hand. Innovative technology for this matter is mainly restricted and dispersed. The available trends and gaps should be explored in this research approach to provide valuable insights into technological environments. Thus, a review is conducted to create a coherent taxonomy to describe the latest research divided into four main categories: development, framework, other hand gesture recognition, and reviews and surveys. Then, we conduct analyses of the glove systems for SLR device characteristics, develop a roadmap for technology evolution, discuss its limitations, and provide valuable insights into technological environments. This will help researchers to understand the current options and gaps in this area, thus contributing to this line of research.

show abstract

Section: Introductionmentioning

confidence: 99%

A Review on Systems-Based Sensory Gloves for Sign Language Recognition State of the Art between 2007 and 2017

Ahmed

Zaidan

et al. 2018

Sensors

188

View full text Add to dashboard Cite

show abstract

“…The training was conducted on 416 images, while testing was performed on 224 images, resulting in a test accuracy of 83.03%. Thalange et al [14] introduced two novel feature extraction techniques, Combined Orientation Histogram and Statistical (COHST) Features and Wavelet Features, to address the recognition of static symbols representing numbers 0 to 9 in American Sign Language. Hand gesture data was collected using a 5-megapixel network camera and processed with different feature extraction methods before input into a neural network for training.…”

Section: Related Workmentioning

confidence: 99%

Data glove-based gesture recognition using CNN-BiLSTM model with attention mechanism

Wu,

Ren,

Song

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

As a novel form of human machine interaction (HMI), hand gesture recognition (HGR) has garnered extensive attention and research. The majority of HGR studies are based on visual systems, inevitably encountering challenges such as depth and occlusion. On the contrary, data gloves can facilitate data collection with minimal interference in complex environments, thus becoming a research focus in fields such as medical simulation and virtual reality. To explore the application of data gloves in dynamic gesture recognition, this paper proposes a data glove-based dynamic gesture recognition model called the Attention-based CNN-BiLSTM Network (A-CBLN). In A-CBLN, the convolutional neural network (CNN) is employed to capture local features, while the bidirectional long short-term memory (BiLSTM) is used to extract contextual temporal features of gesture data. By utilizing attention mechanisms to allocate weights to gesture features, the model enhances its understanding of different gesture meanings, thereby improving recognition accuracy. We selected seven dynamic gestures as research targets and recruited 32 subjects for participation. Experimental results demonstrate that A-CBLN effectively addresses the challenge of dynamic gesture recognition, outperforming existing models and achieving optimal gesture recognition performance, with the accuracy of 95.05% and precision of 95.43% on the test dataset.

show abstract

“…Several frequency domain features have been used in literature for sign language recognition. The main frequencybased techniques used in the literature are dynamic time warping [31], [32], Fourier descriptors [29], [33], Hu moments [29], discrete wavelet transform [34], [35], and wavelet transform [36]. Makhashen et al [7] used the Gabor transform for features extraction.…”

Section: B Vision-based Techniquesmentioning

confidence: 99%

An Efficient Two-Stream Network for Isolated Sign Language Recognition Using Accumulative Video Motion

Luqman

2022

IEEE Access

View full text Add to dashboard Cite

Sign language is the primary communication medium for persons with hearing impairments. This language depends mainly on hand articulations accompanied by nonmanual gestures. Recently, there has been a growing interest in sign language recognition. In this paper, we propose a trainable deep learning network for isolated sign language recognition, which can effectively capture the spatiotemporal information using a small number of signs' frames. We propose a hierarchical sign learning module that comprises three networks: dynamic motion network (DMN), accumulative motion network (AMN), and sign recognition network (SRN). Additionally, we propose a technique to extract key postures for handling the variations in the sign samples performed by different signers. The DMN stream uses these key postures to learn the spatiotemporal information pertaining to the signs. We also propose a novel technique to represent the statical and dynamic information of sign gestures into a single frame. This approach preserves the spatial and temporal information of the sign by fusing the sign's key postures in the forward and backward directions to generate an accumulative video motion frame. This frame was used as an input to the AMN stream, and the extracted features were fused with the DMN features to be fed into the SRN for the learning and classification of signs. The proposed approach is efficient for isolated sign language recognition, especially for recognizing static signs. We evaluated this approach on the KArSL-190 and KArSL-502 Arabic sign language datasets, and the obtained results on KArSL-190 outperformed other techniques by 15% in the signer-independent mode. Additionally, the proposed approach outperformed the state-of-the-art techniques on the Argentinian sign language dataset LSA64. The code is available at https://github.com/Hamzah-Luqman/SLR_AMN.

show abstract

COHST and Wavelet Features Based Static ASL Numbers Recognition

Cited by 16 publications

References 6 publications

A Review on Systems-Based Sensory Gloves for Sign Language Recognition State of the Art between 2007 and 2017

A Review on Systems-Based Sensory Gloves for Sign Language Recognition State of the Art between 2007 and 2017

Data glove-based gesture recognition using CNN-BiLSTM model with attention mechanism

An Efficient Two-Stream Network for Isolated Sign Language Recognition Using Accumulative Video Motion

Contact Info

Product

Resources

About