Efficient Design and Compression of CNN Models for Rapid Character Recognition

Convolutional Neural Networks (CNNs) are extensively utilized for image processing and recognition tasks; however, they often encounter challenges related to large model sizes and prolonged training times. These limitations present difficulties in resource-constrained environments that require rapid model deployment and efficient computation. This study introduces a systematic approach to designing lightweight CNN models specifically for character recognition, emphasizing the reduction of model complexity, training duration, and computational costs without sacrificing performance. Techniques such as hyperparameter tuning, model pruning, and post-training quantization (PTQ) are employed to decrease model size and enhance training speed. The proposed methods are particularly well-suited for deployment on edge computing platforms, such as Raspberry Pi, or embedded systems with limited resources. Our results demonstrate a reduction of over 80% in model size, decreasing from 43.73 KB to 6.25 KB, and a reduction of more than 45% in training time, decreasing from over 150 seconds to less than 80 seconds. This research highlights the potential for achieving a balance between efficiency and accuracy in CNN design for real-world deployment, addressing the increasing demand for streamlined deep learning models in resource-constrained environments.