Article: Implementation of gesture recognition technology optimised by neural networks in OpenMV Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.5 pp.1 - 21 Abstract: Sign language serves as a vital communication medium for the deaf and hard-of-hearing community; yet, existing gesture recognition systems face challenges such as high costs, limited accuracy in complex environments, and hardware dependence. This study presents a novel gesture recognition system leveraging the OpenMV platform, TensorFlow, and EdgeImpulse to address these issues. The proposed system achieves real-time translation of gestures into text with an accuracy exceeding 98%, demonstrating robustness in varying lighting and background conditions. By integrating machine vision capabilities with cost-effective hardware, this system overcomes the limitations of prior methods, such as the reliance on expensive equipment and poor adaptability to real-world scenarios. These findings highlight the system's potential for widespread application in assistive technologies, offering an affordable and efficient solution for improving communication accessibility. Inderscience Publishers - linking academia, business and industry through research

Title: Implementation of gesture recognition technology optimised by neural networks in OpenMV

Authors: Xilong Qu; Siyang Yu; Xiao Tan

Addresses: College of Information Science and Engineering, Changsha Normal University, Changsha, 410000, China ' School of Information Technology and Management, Hunan University of Finance and Economics, Changsha, 410205, China ' School of Information Technology and Management, Hunan University of Finance and Economics, Changsha, 410205, China

Abstract: Sign language serves as a vital communication medium for the deaf and hard-of-hearing community; yet, existing gesture recognition systems face challenges such as high costs, limited accuracy in complex environments, and hardware dependence. This study presents a novel gesture recognition system leveraging the OpenMV platform, TensorFlow, and EdgeImpulse to address these issues. The proposed system achieves real-time translation of gestures into text with an accuracy exceeding 98%, demonstrating robustness in varying lighting and background conditions. By integrating machine vision capabilities with cost-effective hardware, this system overcomes the limitations of prior methods, such as the reliance on expensive equipment and poor adaptability to real-world scenarios. These findings highlight the system's potential for widespread application in assistive technologies, offering an affordable and efficient solution for improving communication accessibility.

Keywords: gesture recognition; neural network; OpenMV; feature extraction.

DOI: 10.1504/IJICT.2025.145155

International Journal of Information and Communication Technology, 2025 Vol.26 No.5, pp.1 - 21

Received: 08 Oct 2024
Accepted: 16 Dec 2024
Published online: 21 Mar 2025 *

Title: Implementation of gesture recognition technology optimised by neural networks in OpenMV

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