  Generative adversarial networks (GANs) have proven hugely successful in
variety of applications of image processing. However, generative adversarial
networks for handwriting is relatively rare somehow because of difficulty of
handling sequential handwriting data by Convolutional Neural Network (CNN). In
this paper, we propose a handwriting generative adversarial network framework
(HWGANs) for synthesizing handwritten stroke data. The main features of the new
framework include: (i) A discriminator consists of an integrated
CNN-Long-Short-Term- Memory (LSTM) based feature extraction with Path Signature
Features (PSF) as input and a Feedforward Neural Network (FNN) based binary
classifier; (ii) A recurrent latent variable model as generator for
synthesizing sequential handwritten data. The numerical experiments show the
effectivity of the new model. Moreover, comparing with sole handwriting
generator, the HWGANs synthesize more natural and realistic handwritten text.
