MatplotAlt: A Python Library for Adding Alt Text to Matplotlib Figures in Computational Notebooks | JoVE Visualize

Area of Science:

Data Visualization
Scientific Computing
Accessibility

Background:

Matplotlib is a widely used Python library for creating static, animated, and interactive visualizations.
Generating descriptive alternative text (alt text) for complex data visualizations remains a challenge, impacting accessibility for visually impaired users.
Existing methods for automated alt text generation often lack accuracy or customization options.

Purpose of the Study:

To introduce MatplotAlt, an open-source Python package designed to simplify the addition of alt text to Matplotlib figures.
To enable Jupyter notebook authors to automatically generate and display chart descriptions with minimal code.
To provide customizable options for alt text generation and display, catering to user preferences and accessibility requirements.

Main Methods:

Developed MatplotAlt as a Python package integrated with Jupyter notebooks.
Implemented heuristic-based and large language model (LLM)-based methods for generating alt text.
Evaluated the accuracy of generated alt text for various Matplotlib figures, including univariate and complex plots.
Investigated methods to improve LLM accuracy, such as prompting with heuristic-based alt text or parsed data tables.

Main Results:

MatplotAlt successfully generates accurate long-form descriptions for both simple and complex Matplotlib figures.
Both heuristic and LLM-based methods within MatplotAlt demonstrate effectiveness in creating descriptive alt text.
State-of-the-art LLMs exhibit factual inaccuracies when describing charts independently.
Prompting GPT-4 Turbo with heuristic-based alt text or data tables significantly enhances description accuracy.

Conclusions:

MatplotAlt offers a practical and efficient solution for enhancing the accessibility of Matplotlib visualizations.
The package empowers researchers and educators to create more inclusive data narratives within Jupyter environments.
Combining heuristic approaches with LLMs, particularly through informed prompting, is crucial for improving the factual accuracy of automated chart descriptions.