Handwriting Optical Character Recognition Using Deep Learning

Abstract

The significance of human writing extends beyond proven character recognition and verification; it plays a crucial role in security and access control. The handwriting of an individual stands out as a paramount biometric attribute, offering a means to authenticate human identity. While various biometric characteristics such as face recognition, fingerprint detection, iris inspection, and retina scanning exist, handwriting holds a unique position.

Voice recognition and handwriting optical character recognition emerge as cutting-edge technologies in the realm of human identity verification. Human handwriting, treated as an image, can be deciphered through computer vision and neural network algorithms. Modern technology paves the way for the development of sophisticated algorithms capable of recognizing and verifying human handwriting.

This paper focuses on handwriting optical character recognition and verification using artificial neural networks and deep learning, incorporating webcams. The process involves capturing human handwriting through a webcam, converting it into image format, and presenting it to the system. These are associated with this research include Web Cam, Image Processing, Artificial Neural Network, and Deep Learning, utilizing Convolutional Neural Networks (CNN) and Artificial Neural Networks (ANN) for effective handwriting optical character recognition and verification.