Abstract: Handwriting identification is a critical technology that bridges handwritten content with digital systems, enabling automation in tasks such as document digitization, form processing, and signature verification. However, handwriting poses challenges such as variability in styles, distortions, and noise, which traditional approaches strug-gle to handle effectively. This study presents a handwriting recognition system using Convolutional Neural Networks (CNNs), a deep learning architecture that excels at extracting spatial features from images. The proposed system is de-signed to recognize handwritten digits, characters, or words with high accuracy. Preprocessing techniques, such as normalization and data augmentation, are applied to ensure the model generalizes well to various handwriting styles and environments. Experiments conducted on benchmark datasets like MNIST and EMNIST demonstrate the effec-tiveness of the model, achieving competitive accuracy and performance.

Keywords: Hand Written, Automation, MNIST, Convolutional Neural Network, EMNIST

| DOI: 10.17148/IARJSET.2025.12116