  This research attempts to construct a network that can convert online and
offline handwritten characters to each other. The proposed network consists of
two Variational Auto-Encoders (VAEs) with a shared latent space. The VAEs are
trained to generate online and offline handwritten Latin characters
simultaneously. In this way, we create a cross-modal VAE (Cross-VAE). During
training, the proposed Cross-VAE is trained to minimize the reconstruction loss
of the two modalities, the distribution loss of the two VAEs, and a novel third
loss called the space sharing loss. This third, space sharing loss is used to
encourage the modalities to share the same latent space by calculating the
distance between the latent variables. Through the proposed method mutual
conversion of online and offline handwritten characters is possible. In this
paper, we demonstrate the performance of the Cross-VAE through qualitative and
quantitative analysis.
