More importantly in today's technology setting, this method reduces the need for human interaction. How can we use the information at hand to make sense of occurrences that at first glance seem unrelated? The ultimate goal of this endeavor is to reconstruct a person's handwriting from scratch. As a key measure in cryptography, handwriting is also an important indication of human characteristics like character. In this research, we demonstrate that Long Short-Term Memory recurrent neural networks may be used to predict data points at the individual level to create complex sequences with extended structure. The method's usefulness is shown with examples drawn from both discrete (text) and continuous (online handwriting) data sets. The network may be used for handwriting synthesis once it has been taught to make predictions based on a text sequence. Several types of realistic cursive handwriting may be generated using the resulting technology.