// the take
An implementation of Alex Graves' handwritten-text generation paper using an LSTM with a mixture density network output layer, built on early TensorFlow (r0.11). It's aimed at people who want to study or tinker with a classic sequence generation model, not at anyone building a production handwriting feature.
The MDN output layer and stroke sampling logic map closely to the original paper, which makes it a useful reference for understanding how Graves' approach actually works in code rather than just equations. It ships a pretrained checkpoint so you can sample and visualize handwriting strokes (several SVG rendering modes with different coloring/weighting) without training anything yourself. The IPython-driven sampling workflow is a nice touch for interactively exploring model output.
Pinned to TensorFlow r0.11 and Python 3 circa 2017 — the checkpoint format and API calls predate TF2 by years, so running this today means either digging up an ancient TF1 environment or rewriting the model code; nothing here works against modern TensorFlow out of the box. Last commit was 2018, no CI, no tests, and no indication anyone maintains it. Training requires the IAM On-Line Handwriting Database, which is gated behind a manual permission request to a third party, so you can't just clone and retrain on your own data without that separate approval. There's no inference-only path that doesn't depend on the full TF1 training stack being resurrected.
We read the README, the directory tree and a key source file in October 2026. Nobody pays to be listed. How we judge →