Handwritten Digit Recognition using Convolutional Neural Network
Prabhas Naidu Mahanti, Chennu Chaitanya, Ashwin Koduri, K Krishna Vamsi, B Purna Shashanka Sai, G Bharathi Mohan · 2023
Handwritten Digit Recognition is an important core topic in computer vision and machine learning with applications ranging from automation to banking and postal services. Convolutional Neural Networks (CNN) are used in this study to take an intriguing trip into the field of Handwritten Digit Recognition (HDR). The task at hand identifying handwritten numbers-may appear simple, but its consequences are far-reaching, ranging from improving the efficiency of financial transactions to automated data entry. our method leverages the capabilities of CNNs, a kind of deep learning model. The model has a good ability to analyze numbers from different types of posts and content. The model demonstrates the accuracy of training and the validity of rigorous analysis, thus revealing the potential.Findings indicate that The model displays a broad knowledge of numbers with different objects, maintains accuracy over noise, and handles different types of simple text. Predictions are shown to demonstrate the model’s capabilities and demonstrate its flexibility in adapting to real-world input data. Confusion matrix analysis can also give a clear idea of the distribution of the model’s resources and provide useful results for focusing improvements. The overall CNN design is a useful tool with practical applications in fields that require accurate and efficient numbers.