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Title: :  Comprehensive Framework for Real-Time Hand Gesture Recognition on Mobile Platforms using Machine Learning,TensorFlow Lite, Keras, MediaPipe, OpenCV and NumPy
PaperId: :  27926
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 12 Issue 1 2026
DUI:    16.0415/IJARIIE-27926
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Roshani Rajesh khobragadePriyadarshini College of Engineering, Hingna, Nagpur
Rutul GanthadePriyadarshini College of Engineering, Hingna, Nagpur
Palak Janbandhu Priyadarshini College of Engineering, Hingna, Nagpur
Aastha NitnawarePriyadarshini College of Engineering, Hingna, Nagpur
Dr. Archana PotnurwarPriyadarshini College of Engineering, Hingna, Nagpur
Amol BamrotwarPriyadarshini College of Engineering, Hingna, Nagpur

Abstract

Information Technology / Computer Engineering / Machine Learning
Numpy, Hand gesture , hand recognition , Machine learning, Hand gesture recognition, mobile vision, MediaPipe, TensorFlow Lite, Keras, OpenCV, NumPy, real-time HCI.
Hand gesture recognition (HGR) has emerged as a natural and intuitive interaction paradigm for human–computer interaction (HCI), particularly in mobile and embedded systems where traditional input mechanisms are constrained. This manuscript presents a detailed and end-to-end framework for real-time hand gesture recognition designed specifically for on-device mobile deployment. The proposed system integrates MediaPipe Hands for efficient landmark extraction, OpenCV and NumPy for vision preprocessing and feature computation, Keras with TensorFlow for deep learning model development, and TensorFlow Lite (TFLite) for optimized on-device inference on Android platforms. A custom dataset was collected from 20 participants under multiple lighting and background conditions, covering both static and dynamic gesture classes. Experimental results demonstrate high recognition accuracy for static gestures (96.8%) and robust performance for dynamic gestures (91.4%), with an average end-to-end latency of approximately 20 ms, enabling real-time execution at around 50 frames per second. Detailed evaluation of accuracy, latency, and resource utilization confirms the feasibility of deploying gesture recognition systems on commodity smartphones. The manuscript also discusses limitations, practical deployment challenges, and future research directions, including multi-hand recognition, adaptive learning, and multimodal interaction.

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IJARIIE Roshani Rajesh khobragade, Rutul Ganthade, Palak Janbandhu , Aastha Nitnaware, Dr. Archana Potnurwar, and Amol Bamrotwar. "Comprehensive Framework for Real-Time Hand Gesture Recognition on Mobile Platforms using Machine Learning,TensorFlow Lite, Keras, MediaPipe, OpenCV and NumPy" International Journal Of Advance Research And Innovative Ideas In Education Volume 12 Issue 1 2026 Page 98-105
MLA Roshani Rajesh khobragade, Rutul Ganthade, Palak Janbandhu , Aastha Nitnaware, Dr. Archana Potnurwar, and Amol Bamrotwar. "Comprehensive Framework for Real-Time Hand Gesture Recognition on Mobile Platforms using Machine Learning,TensorFlow Lite, Keras, MediaPipe, OpenCV and NumPy." International Journal Of Advance Research And Innovative Ideas In Education 12.1(2026) : 98-105.
APA Roshani Rajesh khobragade, Rutul Ganthade, Palak Janbandhu , Aastha Nitnaware, Dr. Archana Potnurwar, & Amol Bamrotwar. (2026). Comprehensive Framework for Real-Time Hand Gesture Recognition on Mobile Platforms using Machine Learning,TensorFlow Lite, Keras, MediaPipe, OpenCV and NumPy. International Journal Of Advance Research And Innovative Ideas In Education, 12(1), 98-105.
Chicago Roshani Rajesh khobragade, Rutul Ganthade, Palak Janbandhu , Aastha Nitnaware, Dr. Archana Potnurwar, and Amol Bamrotwar. "Comprehensive Framework for Real-Time Hand Gesture Recognition on Mobile Platforms using Machine Learning,TensorFlow Lite, Keras, MediaPipe, OpenCV and NumPy." International Journal Of Advance Research And Innovative Ideas In Education 12, no. 1 (2026) : 98-105.
Oxford Roshani Rajesh khobragade, Rutul Ganthade, Palak Janbandhu , Aastha Nitnaware, Dr. Archana Potnurwar, and Amol Bamrotwar. 'Comprehensive Framework for Real-Time Hand Gesture Recognition on Mobile Platforms using Machine Learning,TensorFlow Lite, Keras, MediaPipe, OpenCV and NumPy', International Journal Of Advance Research And Innovative Ideas In Education, vol. 12, no. 1, 2026, p. 98-105. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Comprehensive_Framework_for_Real_Time_Hand_Gesture_Recognition_on_Mobile_Platforms_using_Machine_Learning_TensorFlow_Lite__Keras__MediaPipe__OpenCV_and_NumPy_ijariie27926.pdf (Accessed : ).
Harvard Roshani Rajesh khobragade, Rutul Ganthade, Palak Janbandhu , Aastha Nitnaware, Dr. Archana Potnurwar, and Amol Bamrotwar. (2026) 'Comprehensive Framework for Real-Time Hand Gesture Recognition on Mobile Platforms using Machine Learning,TensorFlow Lite, Keras, MediaPipe, OpenCV and NumPy', International Journal Of Advance Research And Innovative Ideas In Education, 12(1), pp. 98-105IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Comprehensive_Framework_for_Real_Time_Hand_Gesture_Recognition_on_Mobile_Platforms_using_Machine_Learning_TensorFlow_Lite__Keras__MediaPipe__OpenCV_and_NumPy_ijariie27926.pdf (Accessed : )
IEEE Roshani Rajesh khobragade, Rutul Ganthade, Palak Janbandhu , Aastha Nitnaware, Dr. Archana Potnurwar, and Amol Bamrotwar, "Comprehensive Framework for Real-Time Hand Gesture Recognition on Mobile Platforms using Machine Learning,TensorFlow Lite, Keras, MediaPipe, OpenCV and NumPy," International Journal Of Advance Research And Innovative Ideas In Education, vol. 12, no. 1, pp. 98-105, Jan-Feb 2026. [Online]. Available: https://ijariie.com/AdminUploadPdf/Comprehensive_Framework_for_Real_Time_Hand_Gesture_Recognition_on_Mobile_Platforms_using_Machine_Learning_TensorFlow_Lite__Keras__MediaPipe__OpenCV_and_NumPy_ijariie27926.pdf [Accessed : ].
Turabian Roshani Rajesh khobragade, Rutul Ganthade, Palak Janbandhu , Aastha Nitnaware, Dr. Archana Potnurwar, and Amol Bamrotwar. "Comprehensive Framework for Real-Time Hand Gesture Recognition on Mobile Platforms using Machine Learning,TensorFlow Lite, Keras, MediaPipe, OpenCV and NumPy." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 12 number 1 ().
Vancouver Roshani Rajesh khobragade, Rutul Ganthade, Palak Janbandhu , Aastha Nitnaware, Dr. Archana Potnurwar, and Amol Bamrotwar. Comprehensive Framework for Real-Time Hand Gesture Recognition on Mobile Platforms using Machine Learning,TensorFlow Lite, Keras, MediaPipe, OpenCV and NumPy. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2026 [Cited : ]; 12(1) : 98-105. Available from: https://ijariie.com/AdminUploadPdf/Comprehensive_Framework_for_Real_Time_Hand_Gesture_Recognition_on_Mobile_Platforms_using_Machine_Learning_TensorFlow_Lite__Keras__MediaPipe__OpenCV_and_NumPy_ijariie27926.pdf
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