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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: :  27836
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 11 Issue 6 2025
DUI:    16.0415/IJARIIE-27836
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
Aastha Nitnaware Priyadarshini College of Engineering, Hingna, Nagpur
Palak JanbandhuPriyadarshini College of Engineering, Hingna, Nagpur
Amol BamrotwarPriyadarshini College of Engineering, Hingna, Nagpur
Dr. Archna PotnurwarPriyadarshini College of Engineering, Hingna, Nagpur

Abstract

Information Technology / Computer Engineering / Machine Learning
Hand gesture recognition, Real-time mobile gesture detection, MediaPipe Hands, TensorFlow Lite, Keras deep learning, Computer vision, OpenCV, NumPy, Human–computer interaction, Mobile deployment, Landmark extraction, Dynamic gesture recognition
Hand gesture recognition has emerged as an intuitive and natural mode of human–computer interaction, especially for mobile and embedded platforms where traditional input mechanisms are limited. This paper presents a comprehensive framework for real-time hand gesture recognition designed specifically for mobile devices. The system integrates MediaPipe for lightweight and accurate hand-landmark extraction, Keras/TensorFlow for model training, and TensorFlow Lite for optimized on-device inference. OpenCV and NumPy are utilized for image preprocessing, frame handling, and feature engineering. A custom dataset of static and dynamic hand gestures was collected under multiple lighting and background conditions to evaluate real-world performance. The proposed classifier achieved an accuracy of 96.8% for static gestures and 91.4% for dynamic sequences, with an average end-to-end latency of ~20 ms on mid-range Android devices. These results demonstrate the feasibility of deploying low-latency, resource-efficient gesture recognition models on mobile platforms. The study further discusses challenges such as occlusion, lighting variations, multi-hand scenarios, and offers recommendations for improving robustness and expanding gesture vocabularies. The findings highlight the potential of real-time gesture-based interfaces in applications such as accessibility, AR/VR interaction, IoT control, and touchless mobile navigation

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IJARIIE Roshani Rajesh khobragade, Rutul Ganthade, Aastha Nitnaware , Palak Janbandhu, Amol Bamrotwar, and Dr. Archna Potnurwar. "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 11 Issue 6 2025 Page 1482-1483
MLA Roshani Rajesh khobragade, Rutul Ganthade, Aastha Nitnaware , Palak Janbandhu, Amol Bamrotwar, and Dr. Archna Potnurwar. "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 11.6(2025) : 1482-1483.
APA Roshani Rajesh khobragade, Rutul Ganthade, Aastha Nitnaware , Palak Janbandhu, Amol Bamrotwar, & Dr. Archna Potnurwar. (2025). 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, 11(6), 1482-1483.
Chicago Roshani Rajesh khobragade, Rutul Ganthade, Aastha Nitnaware , Palak Janbandhu, Amol Bamrotwar, and Dr. Archna Potnurwar. "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 11, no. 6 (2025) : 1482-1483.
Oxford Roshani Rajesh khobragade, Rutul Ganthade, Aastha Nitnaware , Palak Janbandhu, Amol Bamrotwar, and Dr. Archna Potnurwar. '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. 11, no. 6, 2025, p. 1482-1483. Available from IJARIIE, http://ijariie.com/AdminUploadPdf/Comprehensive_Framework_for_Real_Time_Hand_Gesture_Recognition_on_Mobile_Platforms_using_Machine_Learning__TensorFlow_Lite__Keras__MediaPipe__OpenCV_and_NumPy_ijariie27836.pdf (Accessed : ).
Harvard Roshani Rajesh khobragade, Rutul Ganthade, Aastha Nitnaware , Palak Janbandhu, Amol Bamrotwar, and Dr. Archna Potnurwar. (2025) '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, 11(6), pp. 1482-1483IJARIIE [Online]. Available at: http://ijariie.com/AdminUploadPdf/Comprehensive_Framework_for_Real_Time_Hand_Gesture_Recognition_on_Mobile_Platforms_using_Machine_Learning__TensorFlow_Lite__Keras__MediaPipe__OpenCV_and_NumPy_ijariie27836.pdf (Accessed : )
IEEE Roshani Rajesh khobragade, Rutul Ganthade, Aastha Nitnaware , Palak Janbandhu, Amol Bamrotwar, and Dr. Archna Potnurwar, "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. 11, no. 6, pp. 1482-1483, Nov-Dec 2025. [Online]. Available: http://ijariie.com/AdminUploadPdf/Comprehensive_Framework_for_Real_Time_Hand_Gesture_Recognition_on_Mobile_Platforms_using_Machine_Learning__TensorFlow_Lite__Keras__MediaPipe__OpenCV_and_NumPy_ijariie27836.pdf [Accessed : ].
Turabian Roshani Rajesh khobragade, Rutul Ganthade, Aastha Nitnaware , Palak Janbandhu, Amol Bamrotwar, and Dr. Archna Potnurwar. "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 11 number 6 ().
Vancouver Roshani Rajesh khobragade, Rutul Ganthade, Aastha Nitnaware , Palak Janbandhu, Amol Bamrotwar, and Dr. Archna Potnurwar. 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]. 2025 [Cited : ]; 11(6) : 1482-1483. Available from: http://ijariie.com/AdminUploadPdf/Comprehensive_Framework_for_Real_Time_Hand_Gesture_Recognition_on_Mobile_Platforms_using_Machine_Learning__TensorFlow_Lite__Keras__MediaPipe__OpenCV_and_NumPy_ijariie27836.pdf
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