SMART CASH TERMINAL USING EMBEDDED SYSTEMS FOR AUTOMATED PAYMENT PROCESSING.

Artificial Intelligence (AI) has significantly transformed human-computer interaction, with gesture and vision-based systems offering a new dimension of accessibility and efficiency. The AI Virtual Mouse utilizes computer vision and machine learning algorithms to enable cursor control, click, and scrolling actions through real-time hand gesture recognition using a standard webcam. This innovation eliminates the dependence on physical devices such as traditional mice, thereby enhancing hygiene, convenience, and inclusivity for users with mobility impairments. By employing algorithms such as Convolutional Neural Networks (CNN), MediaPipe Hand Tracking, and Support Vector Machines (SVM), the system achieves high accuracy and responsiveness across diverse environmental conditions. The proposed model further integrates evaluation metrics such as Confusion Matrix, Precision, Recall, F1-Score, and Accuracy to validate performance. Experimental outcomes demonstrate an accuracy of up to 99%, proving the robustness and reliability of the AI-based system. This project emphasizes the potential of intelligent, vision-driven interfaces to revolutionize interaction design and promote seamless, contactless computing experiences..

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-11
DOI
https://doi.org/10.5281/zenodo.24136811
Primary Topic
Hand Gesture Recognition Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

SMART CASH TERMINAL USING EMBEDDED SYSTEMS FOR AUTOMATED PAYMENT PROCESSING.

Arun Kumar, ALAMELU MANGAI M
Zenodo (CERN European Organization for Nuclear Research)
Hand Gesture Recognition Systems
article

SMART CASH TERMINAL USING EMBEDDED SYSTEMS FOR AUTOMATED PAYMENT PROCESSING.

Arun Kumar, ALAMELU MANGAI M
article en

Abstract

Artificial Intelligence (AI) has significantly transformed human-computer interaction, with gesture and vision-based systems offering a new dimension of accessibility and efficiency. The AI Virtual Mouse utilizes computer vision and machine learning algorithms to enable cursor control, click, and scrolling actions through real-time hand gesture recognition using a standard webcam. This innovation eliminates the dependence on physical devices such as traditional mice, thereby enhancing hygiene, convenience, and inclusivity for users with mobility impairments. By employing algorithms such as Convolutional Neural Networks (CNN), MediaPipe Hand Tracking, and Support Vector Machines (SVM), the system achieves high accuracy and responsiveness across diverse environmental conditions. The proposed model further integrates evaluation metrics such as Confusion Matrix, Precision, Recall, F1-Score, and Accuracy to validate performance. Experimental outcomes demonstrate an accuracy of up to 99%, proving the robustness and reliability of the AI-based system. This project emphasizes the potential of intelligent, vision-driven interfaces to revolutionize interaction design and promote seamless, contactless computing experiences..

Zenodo (CERN European Organization for Nuclear Research)
Hand Gesture Recognition Systems
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