Effective Kurdish Sign Language Detection and Classification Using Convolutional Neural Networks

Rawf, Karwan Hama; Abdulrahman, Ayub Othman; Mohammed, Aree Ali

doi:10.21203/rs.3.rs-1965056/v1

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…The lack of resources given to Kurdish sign language has led to a significant problem for researchers in the field, as there are currently few shared databases available for their use. To address this issue, the authors (Rawf, K. H., et al, 2022) have created a dataset for a real-time model recognition using Convolutional Neural Network (CNN) algorithm. The model has been trained and predicted on the KuSL2022 dataset using different activation functions for a number of epochs.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Kurdish Sign Language Recognition Through Random Forest Classifier and Noise Reduction via Singular Value Decomposition (Svd)

Ahmed,

Mahmood,

Mahmood

et al. 2024

SJUOZ

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Deaf people around the world face difficulty communicating with others. Hence, they use their own language to communicate with each other. This paper introduces a new approach for Kurdish sign language recognition using the random forest classifier algorithm aiming to facilitate communication for deaf communities to communicate with others without relying on human interpreters. On the other side, for further enhancement of the images captured during recognition linear algebra techniques have been used such as singular value decomposition for image compression and Moore–Penrose inverse for blur removal. Kurdish language has 34 alphabets and (10 numeric numbers 10, . . . ,3 ,2 ,1). Additionally, three extra signs have been created and added to the dataset, such as space, backspace, and delete sentences for the purpose of real-time translation. A collection of 800 images has been gathered for each character, out of 800 images, only 80 per character were used due to their similar positions but varied alignment, totalling 3,520 images for the dataset (44 characters 80 images each). Two simulation scenarios were carried out: one with optimal conditions - a white background and adequate lighting, and another with challenges such as complex backgrounds and varied lighting angles. Both achieved high match rates of 96% and 87%, respectively. Further, a classification report analyzed precision, recall, and F1 score metrics.

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Section: Related Workmentioning

confidence: 99%

Enhancing Kurdish Sign Language Recognition Through Random Forest Classifier and Noise Reduction via Singular Value Decomposition (Svd)

Ahmed,

Mahmood,

Mahmood

et al. 2024

SJUOZ

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“…Panoradio SDR data was gated, a software-based radio receiver with an analogue-to-digital converter for taking samples of the antenna signal at 250 MHz. The fundamental objective of this study was to develop and demonstrate a generalized modulation recognition algorithm for the type recognition of modulated signals in an environment that contained polluted noise [5,6]. We recommended a method that would begin with extracting and regulating one-of-a-kind data and then move on to classification methods.…”

Section: Introductionmentioning

confidence: 99%

Signal Modulation Recognition System Based on Different Signal Noise Rate Using Artificial Intelligent Approach

Jader

Jumaa

2022

J. Stud. Sci. Eng.

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Everyone has paid much attention to modulation-type recognition in the past few years. There are many ways to find the modulation type, but only a few good ways to deal with signals with a lot of noise. This study comes up with a way to test how well different machine learning algorithms can handle noise when detecting digital and analogue modulations. This study looks at the four most common digital and analogue modulations: Phase Shift Keying, Quadrature Phase Shift Keying, Amplitude Modulation, and Morse Code. A signal noise rate from -10dB to +25dB is used to find these modulations. We used machine learning algorithms to determine the modulation type like Decision Tree, Random Forest, Support Vectors Machine, and k-nearest neighbours. After the IQ samples had been converted to the amplitude of samples and radio frequency format, the accuracy of each method looked good. Still, in the format of the sample phase, each algorithm's accuracy was less. The results show that the proposed method works to find the signals that have noises. When there is less noise, the random forest algorithm gives better results than SVM, but SVM gives better results when there is more noise.

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Sign Language to Sentence Interpreter Using Convolutional Neural Network in Real Time

Seetha,

Christlin Shanuja,

Daniel

et al. 2023

Lecture Notes in Networks and Systems

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Effective Kurdish Sign Language Detection and Classification Using Convolutional Neural Networks

Cited by 3 publications

References 33 publications

Enhancing Kurdish Sign Language Recognition Through Random Forest Classifier and Noise Reduction via Singular Value Decomposition (Svd)

Enhancing Kurdish Sign Language Recognition Through Random Forest Classifier and Noise Reduction via Singular Value Decomposition (Svd)

Signal Modulation Recognition System Based on Different Signal Noise Rate Using Artificial Intelligent Approach

Sign Language to Sentence Interpreter Using Convolutional Neural Network in Real Time

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