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Found 4 papers, 1 papers with code
Improving Handwritten OCR with Training Samples Generated by Glyph Conditional Denoising Diffusion Probabilistic Model
This model conditions on a printed glyph image and creates mappings between printed characters and handwritten images, thus enabling the generation of photo-realistic handwritten samples with diverse styles and unseen text contents.
GlyphControl: Glyph Conditional Control for Visual Text Generation
We evaluate the effectiveness of our approach by measuring OCR-based metrics, CLIP score, and FID of the generated visual text.
Zero-shot Generation of Training Data with Denoising Diffusion Probabilistic Model for Handwritten Chinese Character Recognition
Training from handwritten samples of a small character set, the DDPM is capable of mapping printed strokes to handwritten ones, which makes it possible to generate photo-realistic and diverse style handwritten samples of unseen character categories.
APRNet: Attention-based Pixel-wise Rendering Network for Photo-Realistic Text Image Generation
Style-guided text image generation tries to synthesize text image by imitating reference image's appearance while keeping text content unaltered.
