Spatio-Temporal Handwriting Imitation
This addresses the challenge of realistic cursive handwriting synthesis for applications like document forgery detection or personalized text generation, though it is incremental as it builds on existing methods.
The paper tackles the problem of imitating a person's handwriting by subdividing the process into subtasks, resulting in generated samples that are visually indistinguishable to humans and can partially fool writer identification systems.
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.