Optimizing Deep Learning on Sustainable Embedded Systems: A Study of Handwritten Digit Recognition with CNN and OpenCV

This senior thesis develops a real-time handwritten digit identification system using a Raspberry Pi 3B+ with a camera module, leveraging a lightweight CNN optimized with MNIST. The project highlights the effective implementation of deep learning on edge computing devices through seamless integration of CNN, TensorFlow Lite, and OpenCV's real-time image processing. The system is both cost-effective and precise, enabling real-time digit recognition tasks. This proposed work illustrates the potential of AI applications in education, industry, and commerce, setting the stage for future advancements in embedded AI systems.