HANDSCRIBE- AI-Based Personalized Handwriting Generation using Transformer Models

In an era dominated by digital communication, the art of handwriting is gradually fading, reducing a deeply personal and expressive form of communication to uniform digital text. While typing offers speed and convenience, it lacks the individuality and emotional depth that handwritten text naturally conveys. This project presents an Artificial Intelligence (AI)–based system that learns and reproduces a person’s handwriting style, preserving human uniqueness in a digital environment. Using deep learning and computer vision techniques, the system analyzes handwriting samples to extract style features such as stroke thickness, slant, spacing, and character structure. The proposed system is based on a Transformer-based architecture inspired by Handwriting Transformers, where a Transformer encoder learns the handwriting style from input samples and encodes it into a style representation. This representation is combined with encoded text input through a Transformer decoder to generate realistic handwritten text images. The use of self-attention mechanisms allows the model to capture both global structure and fine-grained stylistic variations in handwriting.