Comparative analysis (Bencmarks) of AI-based font generation projects since 2010.
The table contains a comprehensive comparison of AI-based font generation projects, scrutinising 18 factors of every project. The information is sourced from cited articles.
It is useful when
- Searching for similar projects in your area
- Conducting a survey of the previous projects
- Getting an overview when starting a new Font AI project
Each row represents a different project, and the columns provide detailed information about the project's characteristics.
Metadata
- Citkey: Citation key for referencing the project.
- Short Name: Abbreviated name of the project.
- Name: Full name of the project.
- Year: Year of publication.
Parameters
- Tasks: Primary tasks addressed by the model (e.g., reconstruction, style transfer).
- Input Type: Types of inputs the model uses (e.g., vector paths, raster images).
- Input Detail: Specific details about the input types.
- Encoding Representation: How the input data is encoded.
- Decoding Modality: How the output data is generated or decoded.
- Output Type: Types of outputs the model produces (e.g., vector paths, raster images).
- Output Detail: Specific details about the output types.
- Representation: The representation format used in the model.
- Latent Space: Characteristics of the latent space used in the model, if applicable.
- Datasets Size: Size of the datasets used for training and testing.
- Training / Testing Distribution: Distribution of the data between training and testing sets.
- Dataset Source: Source of the datasets used.
- Techniques and Features: Key techniques and features of the model.
- Architecture Base: The base architecture of the model (e.g., Transformer, RNN).
- Layers: Descriptions of the layers used in the model.
- Output Evaluation Methods: Methods used to evaluate the model's output (e.g., MAE, RMSE).
- Evaluation Comparison: Models or projects used for comparison during evaluation.
- Type Designer Involvement as expert: Involvement of design domain experts in the project.
We welcome contributions from the community to improve and expand this comparison table. If you have information about a project that is not listed or if you find any discrepancies, please feel free to contribute.
If you encounter any issues or have suggestions for improvements, please submit an issue via the GitHub issues page. Provide as much detail as possible to help us understand and address the issue promptly.
- Fork the repository.
- Create a new branch for your changes:
git checkout -b my-new-feature. - Make your changes and commit them:
git commit -am 'Add new feature'. - Push to the branch:
git push origin my-new-feature. - Submit a pull request via the GitHub pull requests page.
This project is licensed under the MIT License. See the LICENSE file for details.
If you use this comparison table in your research, please cite it as follows:
@misc{FontAIGenerationComparison2024,
author = {Filip Paldia},
title = {AI Font Generation Projects: Comparison Table},
year = {2024},
howpublished = {\https://github.com/filipaldi/ai-font-generation-projects/}}
}