Handwritten Character Recognition Using Machine Learning

Handwritten character recognition is a fundamental problem in computer vision with applications in digitization, document processing, and intelligent systems. This work proposes a novel deep learning-based approach for recognizing handwritten characters using a Convolutional Neural Network (CNN). The proposed model leverages convolutional layers for feature extraction, pooling layers for dimensionality reduction, and fully connected layers for classification. The dataset, including MNIST for digits and extensions such as EMNIST for alphanumeric characters, is preprocessed with normalization and augmented to enhance generalization. The architecture is optimized for accuracy and computational efficiency, incorporating techniques such as dropout to mitigate over fitting and adaptive learning rate optimizers for faster convergence. Experiments demonstrate the model's robustness in accurately classifying handwritten characters, achieving state-of-the-art performance on benchmark datasets. This study also explores future integration of advanced techniques like transfer learning, recurrent neural networks for sequence prediction, and deployment on edge devices. The results highlight the potential of deep learning to advance handwritten character recognition in real-world applications.