FPGA-based Neural Network for Arabic and English Handwritten Digit Recognition

Published: 2025
2025 21st International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuits Design (SMACD)
ISBN/ISSN: 2575-4874

Abstract

In recent years, neural networks have been extensively used for handwritten digit recognition. However, limitations in computational efficiency present challenges for real-time applications. This work introduces a neural network model to classify Arabic and English handwritten digits, achieving an accuracy of up to 96.6%. This FPGA-based implementation demonstrates significant performance improvements, achieving a speedup of 14x to 49x over the proposed software, with a power consumption of 1.67 W and latency of 2.395 µs for processing an image of 2.39 Kbytes.