REVIEW 2 cited by
Diffusion models for Handwriting Generation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
In this paper, we propose a diffusion probabilistic model for handwriting generation. Diffusion models are a class of generative models where samples start from Gaussian noise and are gradually denoised to produce output. Our method of handwriting generation does not require using any text-recognition based, writer-style based, or adversarial loss functions, nor does it require training of auxiliary networks. Our model is able to incorporate writer stylistic features directly from image data, eliminating the need for user interaction during sampling. Experiments reveal that our model is able to generate realistic , high quality images of handwritten text in a similar style to a given writer. Our implementation can be found at https://github.com/tcl9876/Diffusion-Handwriting-Generation
Forward citations
Cited by 2 Pith papers
-
Semi-Supervised Adaptation of Diffusion Models for Handwritten Text Generation
A style-embedding masked autoencoder and a semi-supervised training scheme let a handwritten-text diffusion model generate training images for a new, unlabeled handwriting dataset, improving downstream recognition error.
-
Quo Vadis Handwritten Text Generation for Handwritten Text Recognition?
Comparing GAN, diffusion, and autoregressive handwriting generators shows the autoregressive model helps recognition most with very little real data, while the diffusion model wins once more real data is available.
Discussion (0). Continue with ORCID to comment.