sjvasquez/handwriting-synthesis

★ 4,761PythonAudience · researcherComplexity · 3/5Setup · moderate

Mindmap

mindmap
  root((handwriting-synthesis))
    What it does
      Generates SVG handwriting
      Mimics real pen strokes
    Controls
      Bias for neatness
      Style for variation
      Color and line width
    Tech Stack
      Python
      Recurrent neural network
      Pretrained model
    Use Cases
      Sample generation
      Research reference
      Custom handwriting art

mindmap root((handwriting-synthesis)) What it does Generates SVG handwriting Mimics real pen strokes Controls Bias for neatness Style for variation Color and line width Tech Stack Python Recurrent neural network Pretrained model Use Cases Sample generation Research reference Custom handwriting art

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Things people build with this

USE CASE 1

Generate SVG files that look like real handwriting for any text you provide.

USE CASE 2

Customize output style and neatness by adjusting bias and style parameters.

USE CASE 3

Use as a reference implementation of Alex Graves' 2013 handwriting synthesis research.

Tech stack

PythonNumPyTensorFlow

Getting it running

Difficulty · moderate Time to first run · 30min

Requires a compatible Python environment with TensorFlow, import structure is noted as unclean in the README.

In plain English

This project generates realistic-looking handwritten text as image files. You give it a list of text strings and it produces SVG files where that text appears to be written by hand, complete with natural variation in stroke shape and flow. The output is not a font, but something that visually resembles actual handwriting. The approach is based on a research paper from 2013 by Alex Graves that explores how a type of neural network called a recurrent neural network can learn to generate sequential data, including pen strokes. This repository implements those experiments and includes a pretrained model so you can generate samples without training anything yourself. When generating text, you can control two main parameters: bias and style. Bias roughly controls how neat or messy the output looks, with higher values producing cleaner, more deliberate strokes. Style selects among different handwriting patterns the model learned from training data, so different style numbers produce output that looks like it came from different people. You can also set stroke colors and line widths. Using it involves writing a short Python script that creates a Hand object, calls its write method with your text and settings, and receives an SVG file back. A pretrained model is bundled, and a live web demo is linked from the README if you want to try it without running any code. The project was built as a research reference rather than a finished product. The README notes that the import structure is not yet clean and invites contributors who want to package it more neatly or add features like animations. It is a good example of what sequence-generating neural networks can do, but should be treated as research code rather than a polished library.

Copy-paste prompts

Prompt 1

Using the handwriting-synthesis Python library, write a script that creates a Hand object and generates SVG files for the phrases ['Hello World', 'My name is Claude'] with style=3 and bias=0.5.

Prompt 2

I have the handwriting-synthesis repo cloned. Show me how to loop through 5 different style values (0-4) and generate SVG samples for the same text to compare the outputs.

Prompt 3

Using handwriting-synthesis, generate SVG files with different bias settings (0.1, 0.5, 1.0) for the text 'Thank you' and explain what the bias parameter controls.

Prompt 4

How do I change the stroke color and line width when generating handwriting with the sjvasquez/handwriting-synthesis Hand object?

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