Effects of Training Set Expansion in Handwriting Recognition Using Synthetic Data

A perturbation model for the generation of synthetic textlines from existing cursively handwritten lines of text produced by human writers is presented. Our goal is to improve the performance of an off-line cursive handwriting recognition system by providing it with additional synthetic training data. It can be expected that by adding synthetic training data the variability of the training set improves, which leads to a higher recognition rate. On the other hand, synthetic training data may bias a recognizer towards unnatural handwriting styles, which could lead to a deterioration of the recognition rate. In this paper we will show that for certain configurations of the parameters of the recognizer and the synthetic handwriting generation process, the recognition performance can be significantly improved.