Realistic handwriting generation using recurrent neural networks and long short-term networks

Abstract

Generating human-like handwriting by machine from an input text given by the user may seem as an easy task but is very complex in reality. It might not be possible for every human being to write in perfect cursive handwriting because each letter in cursive gets shaped differently depending on what letters surround it, and everyone has a different style of writing. This makes it very difficult to mimic a person’s cursive style handwriting with the help of a machine or even by hand for a matter of fact. This is why signing names in cursive is preferable on any legal documents. In this paper, we will try to use various deep learning methods to generate human-like handwriting. Algorithms using neural networks enable us to achieve this task, and hence, recurrent neural networks (RNN) have been utilized with the aim of generating human-like handwriting. We will discuss the generation of realistic handwriting from the IAM Handwriting Database and check the accuracy of our own implementation. This feat can be achieved by using a special kind of recurrent neural network (RNN), the Long Short-Term Memory networks (LSTM).