Enhanced Handwritten Character Recognition Using Optimised Convolutional Neural Networks

Handwritten character recognition is essential for document automation, assistive technologies, and text digitization. This study suggests a Convolutional Neural Network (CNN)-based framework to classify handwritten characters from gray-scale images. The preprocessing pipeline includes image resizing, normalization, and label encoding. Convolutional, max-pooling, dropout, and dense layers are used in the intended model for feature learning and classification. It is trained using the Adam optimizer for 50 epochs with categorical cross-entropy loss. Post-training, the system detects and segments characters using contour extraction and bounding boxes, enabling automated text reconstruction. Performance is evaluated using accuracy and loss metrics, with visualization techniques applied to analyze results. The proposed method offers an efficient and scalable solution for real-world handwritten text recognition.