HandwritingAgent: Language-Driven Handwriting Synthesis in Scalable Vector Space

• URL: http://arxiv.org/abs/2606.18788v1
• Date: Wed, 17 Jun 2026 08:02:19 GMT
• Title: HandwritingAgent: Language-Driven Handwriting Synthesis in Scalable Vector Space
• Abstract summary: HandwritingAgent is a language-driven agent that can synthesize natural handwriting sequences directly in SVG format with no need for style-specific training.<n>Experiments on diverse handwriting tasks spanning imitation, recognition, multi-lingual handwriting synthesis, and generation of complex handwritten maths and science expressions indicate substantial improvement in performance.
• Score: 12.612154268994184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Teaching machines to emulate natural handwriting styles remains an open challenge, as it requires synthesizing stroke sequences that dynamically vary in shape, texture, pressure and script - not only across individuals, but also within a single person's handwriting. Attempts at this challenge have largely explored deep learning methods in both online and offline settings. However, these approaches are often constrained by style-specific architectural choices, heavy reliance on large datasets, high compute costs, and a lack of flexible control over writing styles through natural language. To this end, we introduce HandwritingAgent, a language-driven agent that can synthesize natural handwriting sequences directly in Scalable Vector Graphics (SVG) format with no need for style-specific training. The agent leverages a large reasoning model to geometrically analyse and autoregressively generate target handwritten glyphs as stroke sequences in a discrete grid canvas environment. Generation is conditioned on texts provided in either conversational or non-conversational mode, along with a reference handwriting-style image. Experiments on diverse handwriting tasks spanning imitation, recognition, multi-lingual handwriting synthesis, and generation of complex handwritten maths and science expressions indicate substantial improvement in performance, with HandwritingAgent matching or surpassing state-of-the-art generative handwriting models, while providing a more efficient, controllable, and generalizable synthesis method.

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