Handwritten Character Recognition Using Deep Learning
Handwritten Character Recognition (HCR) is a significant area of research in the field of pattern recognition and computer vision, aiming to automatically identify and convert handwritten text into machine readable digital formats. Traditional recognition methods often struggle with variations in writing styles, character shapes, and image quality. To address these challenges, this research proposes a deep learning-based handwritten character recognition system that leverages Convolutional Neural Networks (CNNs) for automatic feature extraction and classification. The proposed model is trained on a large dataset of handwritten characters and incorporates preprocessing techniques such as image normalization, noise removal, and segmentation to improve recognition accuracy. Deep learning enables the system to learn complex patterns and hierarchical features directly from raw image data, eliminating the need for manual feature engineering. Experimental results demonstrate that the proposed approach achieves high accuracy, robustness, and generalization across different handwriting styles. The system effectively recognizes handwritten alphabets, numerals, and special characters, making it suitable for applications such as document digitization, postal mail sorting, bank cheque processing, educational assessment systems, and historical manuscript preservation. The study highlights the effectiveness of deep learning techniques in enhancing recognition performance and reducing classification errors compared to conventional machine learning methods. Future enhancements may include multilingual character recognition, real-time processing capabilities, and integration with advanced architectures such as transformers and recurrent neural networks to further improve accuracy and efficiency. Index Terms - Handwritten Character Recognition, Deep Learning, Convolutional Neural Network, Image Processing, Pattern Recognition, Optical Character Recognition, Machine Learning.