Digit Recognition using Convolutional Neural Networks and OpenCV

Abstract

Recognizing handwritten digits accurately is one of the major challenges in computer vision. It plays an important role in real-life applications such as sorting postal codes, processing bank checks, scanning forms, and automating documents. In this study, we built a system that can automatically recognize handwritten digits using Convolutional Neural Networks (CNNs) along with OpenCV for image processing and real-time detection. Our goal was to design a model that performs with high accuracy on both still images and live webcam input.
From the experiments, the system achieved an accuracy of over 90%, showing that it is reliable and performs well in different situations. With the help of OpenCV, it can also identify digits in real time, which makes it suitable for practical use. This project demonstrates how deep learning can make image-based automation more efficient and sets the stage for future systems that can also recognize letters, symbols, and full handwritten text.