Neural handwriting that still imports from demo.py

What it does

This repo implements the handwriting synthesis experiments from Alex Graves’ 2013 paper on sequence generation with recurrent neural networks. It converts plain text into SVG stroke data, letting you tweak visual parameters like stroke color and width. A pretrained model ships with the repo, so you can generate scrawl immediately without running a training loop.

The interesting bit

The clever part is the control surface: a bias dial tightens the penmanship into neat lines or loosens it into a mess, while style priming shifts the overall authorial character. It was built as a reference implementation, yet the generated samples match the quality shown in the original paper.

Key highlights

• Faithful reimplementation of Graves’ Generating Sequences with Recurrent Neural Networks handwriting experiments.
• Includes a pretrained model; no training phase is required to produce output.
• Adjustable bias parameter controls neatness, and style priming alters the visual personality of the script.
• Exports raw SVG strokes with configurable colors and widths.
• Live web demo available to preview results before touching the code.

Caveats

• The Hand class must be imported from demo.py, making the project feel more like a research notebook than an installable library.
• Output is restricted to simple static SVGs; animations or advanced drawing features are not implemented.
• The author openly notes the codebase is research-oriented and welcomes help packaging it for broader use.

Verdict

Grab it if you want a working RNN sequence-generation reference, need procedural handwriting for graphics, or just want to see a 2013 paper brought to life. Look elsewhere if you need a polished library, animated ink, or pip-installable packaging.