Recognition of Handwritten English Alphabets Using a Lightweight Convolutional Neural Network

Recognition System for Handwritten Characters Convolutional neural network customization. A key problem in pattern recognition is handwritten character recognition, which has several applications in fields including license plate recognition, postal automation, and document digitization. In this study, we present a novel Convolutional Neural Network (CNN) architecture that focuses on the 26 uppercase English alphabetic letters, with the aim of effectively recognizing handwritten characters. The model automatically extracts and processes hierarchical information from input images using a sequence of convolutional and pooling layers. We then use fully connected layers for decision-making. Three convolutional layers (32, 64, and 128 filter sizes) make up the design. Maxpooling layers follow, reducing the spatial dimensions without compromising important features. Following the output feature map flattening, the model employs two dense layers. The model uses ReLU activations in layers with 512 and 256 neurons, respectively, to capture intricate interactions. Twenty-six neurones with a softmax activation function for categorization make up the last layer. This architecture strikes a balance between classification accuracy and computing efficiency, specifically for the demands of character recognition. The experimental findings demonstrate the model's capacity to generalise successfully to handwritten characters across various writing styles and noise levels. With common handwritten character datasets, the suggested system achieves competitive accuracy, demonstrating its potential for high accuracy and minimal processing overhead in real-world applications.