Efficient Implementation of a Lightweight AI Classification Model for Handwriting Detection on FPGA

Handwriting recognition is a critical technology for applications like digital document processing and real-time transcription. This paper presents the implementation of a handwriting detection neural network on a Field-Programmable Gate Array (FPGA) to achieve high efficiency and low latency. First the neural network is trained using Keras in Python, and then a quantization algorithm is applied on the parameters of the trained model. The obtained lightweight Neural Network model is used to implement the Verilog model that will be used for the classifier deployment on FPGA. Using quantization and parallel processing strategies, the Network is optimized and then the obtained weights are used in the FPGA-based implementation. Experimental results show that our FPGA-based solution significantly improves processing speed and power consumption efficiency while maintaining competitive recognition accuracy. These achievements indicate the capability of the classifier for real-time handwriting recognition in embedded and edge devices.