Character Recognition By Implementing FPGA-Based Artificial Neural Network

Abstract

A non-linear applied math knowledge modelling tool, Artificial Neural Networks (ANN) are predominantly used to model complicated interactions between inputs and outputs or to look for patterns within the data. Using VHDL coding, we developed a generic hardware-based ANN. This classifier has been trained to recognize letters on a 4x4 binary grid that a user fills out using 16 toggle switches. An LCD shows the most likely classification that the ANN proposed. The ANN was taught to recognize 20 English character patterns and 9 Arabic character patterns on a 4x4 grid to showcase the viability of the FPGA execution of ANN. The Altera DE2 development and education board use the generated design file to flash the Altera Cyclone II FPGA. A training supervisor is also included in the design, which is responsible for training the ANN to recognize a set of 29 standard characters drawn from the English and Arabic alphabets. Positive patterns were found, with the ANN successfully recognizing all training characters. Finally, this study demonstrates the adaptability and boundless possibility of hardware-based implementation of ANN and the infinite possibility of prototyping and building recent systems with VHDL coding on an FPGA platform.