Dactyl Alphabet Modeling and Recognition Using Cross Platform Software

Kondratiuk, Sergii; Krak, Iurii

doi:10.1109/dsmp.2018.8478417

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…Comparing the resulting model with similar works, it can be noted that its accuracy is higher than that of the models presented in [12] and [10], where it is equal to 92,54 % and 97 % respectively.…”

Section: Discussionmentioning

confidence: 76%

“…For instance, in [10,11] deep convolutional neural networks (CN N) are used to classify images with hand gestures. The model proposed in [10] is used to recognize gestures from the Ukrainian dactyl alphabet and achieves an accuracy of 97 %. The model presented in [11] achieves 99 % accuracy in recognizing hand gestures of poststroke people.…”

Section: Approaches To Gesture Recognitionmentioning

confidence: 99%

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Recognizing gestures of the ukrainian dactylic alphabet

Глибовець,

Бікчентаєв

2023

PC&I

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Sign language is a visual way of communicating used by people who are deaf or hard of hearing. It involves handshapes, facial expressions, and body movements to convey meaning. Sign language helps the deaf community interact with each other and the hearing world, allowing them to participate fully in society. According to the WHO (World Health Organization) over 5 % of the world’s population – or 430 million people — experience problems with hearing. More than 44,000 people with hearing impairments are registered with the Ukrainian Society of the Deaf, an all-Ukrainian public organization for the disabled. Therefore, it is extremely important to develop new software, available to the public, that would allow quickly and effectively learn and understand sign language. This work aims to review gesture recognition techniques and develop a system for detecting and classifying gestures of the Ukrainian dactylic alphabet. Two main approaches to gesture recognition, glove-based and computer vision-based (CV), are explained, with the latter being preferred due to its flexibility and widespread usage. The text elaborates on deep learning-based approaches, particularly LSTM networks, and the advantages they offer in automatically learning features from raw image data. The process of creating a dataset for training the gesture classification model is described, which involves recording videos of hand gestures and extracting keypoints using Google MediaPipe. The model training phase is detailed, covering the architecture of the LSTM-based classifier, optimization algorithms, and loss functions. The resulting model achieves an accuracy of 98.4% on the test dataset. A program for real-time gesture recognition is developed using Python and relevant libraries. The program utilizes a webcam feed to detect and classify hand gestures, displaying the top three predicted letters of the Ukrainian dactylic alphabet. The scientific novelty of the obtained results: the paper presents a method that utilizes hand keypoints for recognizing hand gestures of the Ukrainian dactyl alphabet. Also, as part of the development of the gesture recognition system, a data set was collected, where each gesture corresponds to 50 videos of 65 frames. The practical significance of the results obtained: the model obtained as a result of the study can be used to interpret the gestures of the Ukrainian dactylic alphabet. The dataset collected for training this model can be used in other works to train or validate similar models. The paper might be of use to the ones who are interested in developing similar systems for gesture recognition.

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Section: Discussionmentioning

confidence: 76%

Section: Approaches To Gesture Recognitionmentioning

confidence: 99%

Recognizing gestures of the ukrainian dactylic alphabet

Глибовець,

Бікчентаєв

2023

PC&I

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“…A number of studies are devoted to the ascertainment of a probable mechanism of laser emission on biological objects of different levels of organization from cells to a holistic organism [23,27]. Moreover, all researchers point out that the effectiveness of laser emission, with the wavelength and emission power, significantly depends on the dose of emission t J D   and the general effect may have both activating and inhibiting effects on biological structures [8,28].…”

Section: Setting the Taskmentioning

confidence: 99%

Optimization of Resource Allocation, Exposure Time and Rotary Speed of Incubative Eggs

Milenin¹,

Лисиченко²,

Milenin³

et al. 2023

IAPGOS

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Recently, the laser technology of influencing biological objects in biology, medicine, and veterinary medicine has become widespread in order to activate certain biochemical and physiological processes in the organism. Any influence of electromagnetic radiation (in particular optical emission) requires the exact adherence to the recommended illumination dose to obtain a positive effect on the biological object. The article presents the results of a theoretical study concerning provision of uniform illumination of the egg’s surface, taking into account the location of the laser radiation source and rotating time of the egg.

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“…Pattern recognition is a relevant and promising area in the field of information technology, the scope of its application is expanding every year. In turn, recognition [4,5] has separate tasks that should be solved in order to obtain the most accurate result. This includes the recognition process itself, image preprocessing to detect and eliminate noise, segmentation, real-time recognition, etc.…”

Section: Introductionmentioning

confidence: 99%

Local Difference Threshold Learning in Filtering Normal White Noise

Timchenko

Kokriatskaia

Tverdomed

et al. 2023

IAPGOS

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The article was aimed at studying the process of learning by the local difference threshold when filtering normal white noise. The existing learning algorithms for image processing were analyzed and their advantages and disadvantages were identified. The influence of normal white noise on the recognition process is considered. A method for organizing the learning process of the correlator with image preprocessing by the GQP method has been developed. The dependence of the average value of readings of the rank CCF (RCCF) of GQPs of the reference and current images, representing realizations of normal white noise, on the probability of formation of readings of zero GQP is determined. Two versions of the learning algorithm according to the described learning method are proposed. A technique for determining the algorithm efficiency estimate is proposed.

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Dactyl Alphabet Modeling and Recognition Using Cross Platform Software

Cited by 5 publications

References 11 publications

Recognizing gestures of the ukrainian dactylic alphabet

Recognizing gestures of the ukrainian dactylic alphabet

Optimization of Resource Allocation, Exposure Time and Rotary Speed of Incubative Eggs

Local Difference Threshold Learning in Filtering Normal White Noise

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