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Sdt

SDT is a Python and PyTorch research implementation of a style-disentangled Transformer that generates online handwriting conditioned on content and style, representing writer-level and character-level styles separately.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Sdt — OpenViST project profile in Image & Video AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use SDT when you want a code-based research workflow for training, generating, or evaluating style-conditioned handwriting rather than a hosted text-to-handwriting tool.

Who it is for

  • Researchers studying handwriting generation and style representation
  • Developers prototyping conditional handwriting-generation workflows

Documented uses

  • Train the implementation on documented Chinese or Japanese handwriting datasets.
  • Generate characters with a trained model.
  • Evaluate generated samples.
  • Explore how writer-level style can be separated from character-level variation.
Before you choose

Requirements & limits

The repository is public and licensed under MIT. Its documented setup uses Python 3.8, Conda, and the dependencies in environment.yml.

  • The primary workflow is code-based and requires preparing a local Python environment, datasets, and model weights.
  • The repository describes datasets for Chinese, Japanese, and English, while its README names Chinese and Japanese training commands.
  • The project’s main method generates online handwriting; an offline-to-offline Chinese extension is also described.
  • Training, generation with a trained model, and sample evaluation are represented by separate scripts.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the SDT Python environment

Create the documented Python 3.8 environment and install the repository dependencies.

Before you start

  • A local copy of the SDT repository
  • Conda
  • Python 3.8 environment specification in environment.yml

Steps

  1. Create and activate a Conda environment named sdt with Python 3.8.

  2. Install the repository dependencies by creating the environment from environment.yml.

Expected outcome

A Conda environment named sdt with the repository dependencies installed.

Guide limitations

  • This covers environment setup only; it does not train a model or generate handwriting.
  • The README does not specify hardware requirements.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
15 October 2026
Original website title
dailenson/SDT · Official homepage title · retained in its source language
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