Handwritten character recognition using CNN
Harshita Singh, Sudhir Kumar Singh, Amar Kumar Mohapatra · 2025
Pattern recognition, which is the field in which computers study and recognize characters based on patterns from diverse sources, includes handwritten character recognition. Even with advancements in technology, humans continue to outperform machines in activities involving pattern identification. Convolutional neural networks (CNNs), in particular, are key components of Deep Learning, which is used to solve self-perception issues such as picture and handwriting recognition. The goal of this research is to use the dataset that has 26000 images of the English characters called A_Z dataset to train CNNs for handwritten recognition and our study demonstrates high accuracy of up to 98% using CNN. CNNs have been made possible by advances in image labelling, object detection, and scene classification. Numerous techniques, including SVM and logistic regression, are used in classification. High detection rates have been attained using CNNs, particularly on large datasets such as CIFAR10 and ImageNet. These multilayer networks are as accurate in categorization and prediction as human beings.