6 papers with code • 0 benchmarks • 8 datasets
The inverse of handwriting recognition. From text generate and image of handwriting (offline) of trajectory of handwriting (online).
These leaderboards are used to track progress in Handwriting generation
This is especially true for handwritten text recognition (HTR), where each author has a unique style, unlike printed text, where the variation is smaller by design.
We introduce the Professor Forcing algorithm, which uses adversarial domain adaptation to encourage the dynamics of the recurrent network to be the same when training the network and when sampling from the network over multiple time steps.
Digital ink promises to combine the flexibility and aesthetics of handwriting and the ability to process, search and edit digital text.
This paper presents a GAN for generating images of handwritten lines conditioned on arbitrary text and latent style vectors.