  This paper presents a novel approach to generate synthetic dataset for
handwritten word recognition systems. It is difficult to recognize handwritten
scripts for which sufficient training data is not readily available or it may
be expensive to collect such data. Hence, it becomes hard to train recognition
systems owing to lack of proper dataset. To overcome such problems, synthetic
data could be used to create or expand the existing training dataset to improve
recognition performance. Any available digital data from online newspaper and
such sources can be used to generate synthetic data. In this paper, we propose
to add distortion/deformation to digital data in such a way that the underlying
pattern is preserved, so that the image so produced bears a close similarity to
actual handwritten samples. The images thus produced can be used independently
to train the system or be combined with natural handwritten data to augment the
original dataset and improve the recognition system. We experimented using
synthetic data to improve the recognition accuracy of isolated characters and
words. The framework is tested on 2 Indic scripts - Devanagari (Hindi) and
Bengali (Bangla), for numeral, character and word recognition. We have obtained
encouraging results from the experiment. Finally, the experiment with Latin
text verifies the utility of the approach.
