Urdu Handwriting Generation using Generative Adversarial Networks

Abstract

Deep learning has significantly improved handwriting text recognition, esp. for Latin scripts. Arabic scripts including Urdu is a family of complex scripts and they pose difficult challenges for deep learning architectures. Data avail ability is a significant obstacle in developing Urdu handwriting recognition systems. Since gathering data is a costly and challenging task, there is a need to increase training data using novel approaches. One possible solu tion is to make a model that can generate similar yet different samples from the existing data samples. In this paper, we propose such models based on Generative Adversarial Networks (GANs) that have the ability to synthe size realistic samples similar to the original dataset. The generator is class conditioned to produce Urdu samples of varying characters that differ in style. Visual and quantitative analysis convey that generated samples are of realistic nature and can be used to increase datasets. Synthesized samples integrated with the existing training set is shown to increase the performance of a handwriting recognition model.