Spatio-Temporal Handwriting Imitation

Abstract

Most people think that their handwriting is unique and cannot be imitated by machines, especially not using completely new content. Current cursive handwriting synthesis is visually limited or needs user interaction. We show that subdividing the process into smaller subtasks makes it possible to imitate someone's handwriting with a high chance to be visually indistinguishable for humans. Therefore, a given handwritten sample will be used as the target style. This sample is transferred to an online sequence. Then, a method for online handwriting synthesis is used to produce a new realistic-looking text primed with the online input sequence. This new text is then rendered and style-adapted to the input pen. We show the effectiveness of the pipeline by generating in- and out-of-vocabulary handwritten samples that are validated in a comprehensive user study. Additionally, we show that also a typical writer identification system can partially be fooled by the created fake handwritings.

arXiv commentMain paper: 16 pages, supplemental material: 7 pages

References in corpus (4)

• A Recurrent Latent Variable Model for Sequential Data
• DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images
• Context-Aware Text-Based Binary Image Stylization and Synthesis
• DeepWriting: Making Digital Ink Editable via Deep Generative Modeling

Cited by in corpus (4)

• Content and Style Aware Generation of Text-line Images for Handwriting Recognition
• Zero-Shot Paragraph-level Handwriting Imitation with Latent Diffusion Models
• Diffusion models for Handwriting Generation
• SmartPatch: Improving Handwritten Word Imitation with Patch Discriminators