Korean Sign Language Alphabet Recognition Through the Integration of Handcrafted and Deep Learning-Based Two-Stream Feature Extraction Approach

Shin, Jungpil; Miah, Abu Saleh Musa; Akiba, Yuto; Hirooka, Koki; Hassan, Najmul; Hwang, Yong Seok

doi:10.1109/access.2024.3399839

Cited by 3 publications

(1 citation statement)

References 54 publications

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“…Researchers have utilized various datasets, tools and techniques to make a system for AD classification. Recently, researchers have focused on two main approaches for training and classifying models, conventional learning and deep learning, as they proved excellent in other domains [ 28 , 29 , 30 ]. Recent studies like [ 24 , 31 , 32 , 33 , 34 ] used conventional learning for AD classification, while studies like [ 5 , 35 , 36 , 37 , 38 ] employed deep learning techniques.…”

Section: Related Workmentioning

confidence: 99%

Residual-Based Multi-Stage Deep Learning Framework for Computer-Aided Alzheimer’s Disease Detection

Hassan,

Musa Miah,

Shin

2024

J. Imaging

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Alzheimer’s Disease (AD) poses a significant health risk globally, particularly among the elderly population. Recent studies underscore its prevalence, with over 50% of elderly Japanese facing a lifetime risk of dementia, primarily attributed to AD. As the most prevalent form of dementia, AD gradually erodes brain cells, leading to severe neurological decline. In this scenario, it is important to develop an automatic AD-detection system, and many researchers have been working to develop an AD-detection system by taking advantage of the advancement of deep learning (DL) techniques, which have shown promising results in various domains, including medical image analysis. However, existing approaches for AD detection often suffer from limited performance due to the complexities associated with training hierarchical convolutional neural networks (CNNs). In this paper, we introduce a novel multi-stage deep neural network architecture based on residual functions to address the limitations of existing AD-detection approaches. Inspired by the success of residual networks (ResNets) in image-classification tasks, our proposed system comprises five stages, each explicitly formulated to enhance feature effectiveness while maintaining model depth. Following feature extraction, a deep learning-based feature-selection module is applied to mitigate overfitting, incorporating batch normalization, dropout and fully connected layers. Subsequently, machine learning (ML)-based classification algorithms, including Support Vector Machines (SVM), Random Forest (RF) and SoftMax, are employed for classification tasks. Comprehensive evaluations conducted on three benchmark datasets, namely ADNI1: Complete 1Yr 1.5T, MIRAID and OASIS Kaggle, demonstrate the efficacy of our proposed model. Impressively, our model achieves accuracy rates of 99.47%, 99.10% and 99.70% for ADNI1: Complete 1Yr 1.5T, MIRAID and OASIS datasets, respectively, outperforming existing systems in binary class problems. Our proposed model represents a significant advancement in the AD-analysis domain.

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Section: Related Workmentioning

confidence: 99%

Residual-Based Multi-Stage Deep Learning Framework for Computer-Aided Alzheimer’s Disease Detection

Hassan,

Musa Miah,

Shin

2024

J. Imaging

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A multi-lingual sign language recognition system using machine learning

Najib

2024

Multimed Tools Appl

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Recently, automatic sign language recognition field gets a great attention. Machine learning algorithms are mainly used to recognize hand gestures. Most of recent studies train their machine learning model using a specific sign language of a specific country such as the American Sign Language. In this paper, we propose a multi-lingual sign language system based machine learning that is called Multi-lingual Sign Languages Interpreter (MSLI) system. MSLI trains a machine learning model based on hand signs of multiple languages. It can detect the language of the input signs and their labels. In a case of input testing signs with the same language, the proposed system can provide two-steps recognition, where it only detects the language of the first sign, and then the rest signs are tested according to the recognized language. Also, MSLI can provide separate classification of signs per each language. Experiments were performed using 11 datasets with different languages. Separate and combined classification was performed on the input data. Experimental results show the accuracy of the proposed system. Training accuracy of the proposed system over most of the used separate different sign language datasets is approximately ranged from 90 to 100%. Also, most classification accuracy results of the test data of the separate datasets exceeded 90%. The combined classification of proposed MSLI archived training accuracy of 95.87% and testing accuracy of 92.33%.

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Handwritten Recognition Techniques: A Comprehensive Review

Alhamad,

Shehab,

Shambour

et al. 2024

Symmetry

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Given the prevalence of handwritten documents in human interactions, optical character recognition (OCR) for documents holds immense practical value. OCR is a field that empowers the translation of various document types and images into data that can be analyzed, edited, and searched. In handwritten recognition techniques, symmetry can be crucial to improving accuracy. It can be used as a preprocessing step to normalize the input data, making it easier for the recognition algorithm to identify and classify characters accurately. This review paper aims to summarize the research conducted on character recognition for handwritten documents and offer insights into future research directions. Within this review, the research articles focused on handwritten OCR were gathered, synthesized, and examined, along with closely related topics, published between 2019 and the first quarter of 2024. Well-established electronic databases and a predefined review protocol were utilized for article selection. The articles were identified through keyword, forward, and backward reference searches to comprehensively cover all relevant literature. Following a rigorous selection process, 116 articles were included in this systematic literature review. This review article presents cutting-edge achievements and techniques in OCR and underscores areas where further research is needed.

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Korean Sign Language Alphabet Recognition Through the Integration of Handcrafted and Deep Learning-Based Two-Stream Feature Extraction Approach

Cited by 3 publications

References 54 publications

Residual-Based Multi-Stage Deep Learning Framework for Computer-Aided Alzheimer’s Disease Detection

Residual-Based Multi-Stage Deep Learning Framework for Computer-Aided Alzheimer’s Disease Detection

A multi-lingual sign language recognition system using machine learning

Handwritten Recognition Techniques: A Comprehensive Review

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