Handwriting Generation and Synthesis: A Review

Abstract

For generations, the handwritten text has been a key medium of communication and a foundation of our culture and education, and it is frequently considered an art form. It has been discovered to aid in tasks such as notetaking and reading whilst writing, as well as improving short and long-term memory. Although there are fewer applications for handwriting synthesizing, this challenge can be generalized and functionally applied to other, more practical challenges. Handwriting synthesis is a difficult task to accomplish. This paper presents a systematic overview of how various algorithms particularly Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTMs) recurrent neural networks, Reinforcement Learning (RL), and Generative Adversarial Networks (GANs) have been used to generate handwriting for the given text.