Experiment on Handwriting Generation with Recurrent Neural Networks using Small Datasets

Abstract

This paper examines the performance of a specific Long-Short Term Memory Recurrent Neural Network for cursive handwriting generation when training with small datasets. The RNN can generate complex structure sequences by predicting one data point at a time. Then, by predicting the overall writing structure, the handwriting can be synthesized. The resulting network can generate different handwriting style parameters.