The implementation of neural networks capable of possessing real-time applications such as character recognition faces challenges due to their resource-intensive nature. In this work, a cascading methodology for neural network implementation is proposed with the aim of enhancing resource efficiency namely neuron count. Reducing the neuron count allows implementation larger neural networks on FPGA boards, meeting the resource constraints such as LUTs, DSPs, RAM, I/O ports, etc. This work uses Vivado Design Suite and Python to implement Deep Neural Networks (DNN) for the efficient recognition of handwritten characters in three languages: Hindi, Tamil, and English. One well-liked platform for synthesis and FPGA implementation is Vivado Design Suit. This cascade method’s precision and number of neurons is contrasted with a single neural network that classifies input straight into the appropriate letters. According to the results, the suggested work maintains accuracy over 90% while reducing the number of neurons by over 13%. This method of recognizing multilingual handwritten characters shows promise for real-world use in robots or surveillance, where a replacement computer vision chip might be created.