A comparison of convolutional neural networks for Kazakh sign language recognition

Kenshimov, Chingiz; Mukhanov, S. B.; Merembayev, Timur; Yedilkhan, Didar

doi:10.15587/1729-4061.2021.241535

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…Image recognition plays a vital role in different areas, such as gesture recognition [18], autonomous tomato harvesting [19], medicine issues [20], image steganography [21], and remote sensing [22]. Image recognition and handwriting recognition neural networks face challenges related to the variability and complexity of the input images.…”

Section: Methodsmentioning

confidence: 99%

Improving OCR Accuracy for Kazakh Handwriting Recognition Using GAN Models

2023

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This paper aims to increase the accuracy of Kazakh handwriting text recognition (KHTR) using the generative adversarial network (GAN), where a handwriting word image generator and an image quality discriminator are constructed. In order to obtain a high-quality image of handwritten text, the multiple losses are intended to encourage the generator to learn the structural properties of the texts. In this case, the quality discriminator is trained on the basis of the relativistic loss function. Based on the proposed structure, the resulting document images not only preserve texture details but also generate different writer styles, which provides better OCR performance in public databases. With a self-created dataset, images of different types of handwriting styles were obtained, which will be used when training the network. The proposed approach allows for a character error rate (CER) of 11.15% and a word error rate (WER) of 25.65%.

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Section: Methodsmentioning

confidence: 99%

Improving OCR Accuracy for Kazakh Handwriting Recognition Using GAN Models

2023

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“…Models of architecture neural network presents a machine learning method in a deep neural network. EffectiveNet is a composite scaling method, which consistently improves model accuracy and efficiency for scaling existing models such as MobileNet (+1.4 % image fidelity) and ResNet (+0.7 %) over traditional scaling methods [13,14].…”

Section: Architecture and Modelsmentioning

confidence: 99%

Gesture Recognition of Machine Learning and Convolutional Neural Network Methods for Kazakh Sign Language

Mukhanov,

Uskenbayeva,

Young Im Cho

et al. 2023

sjaitu

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Recently, there has been a growing interest in machine learning and neural networks among the public, largely due to advancements in technology which have led to improved methods of computer recognition of objects, sounds, texts, and other data types. As a result, human-computer interactions are becoming more natural and comprehensible to the average person. The progress in computer vision has enabled the use of increasingly sophisticated models for object recognition in images and videos, which can also be applied to recognize hand gestures. In this research, popular hand gesture recognition models, such as the Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Support Vector Machine (SVM) were examined. These models vary in their approaches, processing time, and training data size. The important feature of this research work is the use of various machine learning algorithms and methods such as CNN, LSTM, and SVM. Experiments showed different results when training neural networks for sign language recognition in the Kazakh sign language based on the dactyl alphabet. This article provides a detailed description of each method, their respective purposes, and effectiveness in terms of performance and training. Numerous experimental results were recorded in a table, demonstrating the accuracy of recognizing each gesture. Additionally, specific hand gestures were isolated for testing in front of the camera to recognize the gesture and display the result on the screen. An important feature was the use of mathematical formulas and functions to explain the working principle of the machine learning algorithm, as well as the logical scheme and structure of the LSTM algorithm.

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“…Most of the early research was based on numerical simulation methods; however, in the last decade, breakthroughs in computer vision (CV) (Buribayev et al, 2021, [25], Kenshimov et al [26]) and natural language processing (NLP) have led to the rapid development of artificial neural networks (ANNs) (Yeleussinov et al [27]). An ANN takes input (e.g., a series of images) and outputs a prediction based on the task (e.g., image recognition).…”

Section: Introductionmentioning

confidence: 99%

Application of Physics-Informed Neural Networks to River Silting Simulation

Omarova,

Amirgaliyev,

Kozbakova

et al. 2023

Applied Sciences

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Water resource pollution, particularly in river channels, presents a grave environmental challenge that necessitates a comprehensive and systematic approach encompassing assessment, forecasting, and effective management. This article provides a comprehensive exploration of the methodology and modeling tools employed to scrutinize the process of river channel pollution due to silting, rooted in the fundamental principles of hydrodynamics and pollutant transport dynamics. The study’s methodology seamlessly integrates numerical simulations with state-of-the-art neural network techniques, with a specific focus on the physics-informed neural network (PINN) method. This innovative approach represents a groundbreaking fusion of artificial neural networks (ANNs) and physical equations, offering a more efficient and precise means of modeling a wide array of complex processes and phenomena. The proposed mathematical model, grounded in the Euler equation, has been meticulously implemented using the Ansys Fluent software package, ensuring accuracy and reliability in the computations. In a pivotal phase of the research, a thorough comparative analysis was conducted between the results derived using the PINN method and those obtained using conventional numerical approaches with the Ansys Fluent software package. The outcomes of this analysis revealed the superior performance of the PINN method, characterized by the generation of smoother pressure fluctuation profiles and a significantly reduced computation time, underscoring its potential as a transformative modeling tool. The calculated data originating from this study assume paramount significance in the ongoing battle against river sedimentation. Beyond this immediate application, these findings also serve as a valuable resource for creating predictive materials pertaining to river channel silting, thereby empowering decision-makers and environmental stakeholders with essential information. The utilization of modeling techniques to address pollution concerns in river channels holds the potential to revolutionize risk management and safeguard the integrity of our vital water resources. However, it is imperative to underscore that the effectiveness of such models hinges on ongoing monitoring and frequent data updates, ensuring that they remain aligned with real-world conditions. This research not only contributes to the enhanced understanding and proactive management of river channel pollution due to silting but also underscores the pivotal role of advanced modeling methodologies in the preservation of our invaluable water resources for present and future generations.

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A comparison of convolutional neural networks for Kazakh sign language recognition

Cited by 6 publications

References 12 publications

Improving OCR Accuracy for Kazakh Handwriting Recognition Using GAN Models

Improving OCR Accuracy for Kazakh Handwriting Recognition Using GAN Models

Gesture Recognition of Machine Learning and Convolutional Neural Network Methods for Kazakh Sign Language

Application of Physics-Informed Neural Networks to River Silting Simulation

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