Synthetic data generation for Indic handwritten text recognition

TL;DR

This paper introduces a method to generate synthetic handwritten text data for Indic scripts by applying distortions to digital text, enhancing training datasets and improving recognition accuracy.

Contribution

It proposes a novel distortion-based synthetic data generation technique for Indic handwritten text recognition, validated on Devanagari, Bengali, and Latin scripts.

Findings

Improved recognition accuracy with synthetic data

Effective augmentation of existing datasets

Validated on multiple Indic scripts and Latin text

Abstract

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.