Abstract
The limited computational resources of mobile devices significantly constrain their ability to effectively execute resource-intensive computer vision tasks, such as , video processing, augmented reality, and face recognition. Handwritten character recognition (HCR) is a critical application of machine learning and computer vision, with wide-ranging implications for digital transformation and accessibility. This proposed study explores the implementation of a deep learning-based HCR system tailored for mobile phones using cloudlet. A cloudlet is a novel technology aimed at boosting the computational power of mobile devices to manage resource-intensive tasks. It enables mobile clients to interact with nearby servers, allowing only minimally processed data to be transmitted from the mobile application to the server. The server handles the complex computations and sends the results back to the mobile device in real-time. By utilizing Convolutional Neural Networks (CNNs), the proposed method achieves high accuracy and efficiency on resource-constrained mobile devices. The research details the system architecture, dataset preprocessing, model training, and integration into an Android application, offering a robust solution for real-time handwritten character recognition.
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