Artificial intelligence techniques including deep learning play a major role in all fields and in line with the advancement in technology. Handwritten digit recognition is an important issue in the field of computer vision, which is used in wide applications such as optical character recognition and handwritten digits. In the current research, we describe a unique deep learning technique that uses a Convolutional Neural Network (CNN) framework with better normalization algorithms and adjusted hyperparameters for improved efficiency as well as generalize. Contrasting conventional techniques, our methodology concentrates on minimizing overfitting through the use of adjustable rate of abandonment and innovative pooling procedures, resulting in greater accuracy in handwriting number classification. Following considerable research, the recommended approach obtains an outstanding classification accuracy of 99.03%, proving its ability to recognize intricate structures in handwritten numbers. The approach's usefulness is reinforced by a complete review of measures including recall, accuracy, F1 score, as well as confuse matrix assessment, which show improvements throughout all digit categories. . The results of the investigation highlight the innovative conceptual layout and optimization methodologies used, representing a substantial leap in the realm of number identification.
link
https://doi.org/10.54216/FPA.170226