fastcdm 0.1.3

Fast CDM utilities for LaTeX tokenization, rendering, and matching

⚡️FastCDM

[GitHub Repo] | [HuggingFace Spaces]

🚀 Introduction

CDM ensures the objectivity and accuracy of evaluation by rendering predicted and ground-truth LaTeX formulas into images, and then using visual feature extraction and localization techniques to perform precise character-level matching, combined with spatial position information.

FastCDM aims to address performance issues. As a high-performance optimized version of the original CDM, FastCDM employs the browser-based KaTeX rendering engine instead of traditional LaTeX compilation, resulting in significantly improved speed.

🎯 Project Goals

The core objective of FastCDM is to provide a convenient user experience during the training process, helping to advance formula recognition tasks. We are committed to:

• Providing simple and easy-to-use API interfaces for convenient integration of evaluation within the training loop.
• Supporting both real-time evaluation and batch evaluation modes.
• Providing visualization tools for evaluation metrics during the training process.

Why Choose FastCDM?

1. Extreme Performance: Based on the KaTeX rendering engine, it is tens of times faster than the traditional LaTeX compilation process.
2. Simplified Deployment: No need to install complex LaTeX environments (ImageMagick, texlive-full, etc.).
3. Accurate Evaluation: Adopts character detection matching methods to avoid the unfairness issues associated with traditional text metrics.
4. Continuous Optimization: Supplements and refines CDM symbol support, with continuous iterative improvements.
5. Easy Integration: Provides a unified API interface for easy integration into various training frameworks. Future integration with mainstream training frameworks such as PyTorch and Transformers is planned.

⚠️ Note

Although KaTeX is extremely fast, it is a lightweight solution optimized for the Web and cannot support 100% of all obscure LaTeX syntax.

For the vast majority of standard formulas, it performs perfectly. This is a reasonable and sustainable technical choice.

You can check KaTeX's support coverage here: 🔗 KaTeX Support Table

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Usage

Installation

You need to install node.js and chromedriver in advance.

• For node.js installation, please refer to here.
• For chromedriver installation, please refer to here.

pip install fastcdm

Quick Start

from fastcdm import FastCDM

chromedriver_path = "driver/chromedriver"

# Initialize FastCDM evaluator
evaluator = FastCDM(chromedriver_path=chromedriver_path)

# Evaluate
cdm_score, recall, precision = evaluator.compute(gt="E = mc^2", pred="E + 1 = mc^2", visualize=False)

# Evaluate and visualize
cdm_score, recall, precision, vis_img = evaluator.compute(gt="E = mc^2", pred="E + 1 = mc^2", visualize=True)

Interactive Demo

We provide a visualization Demo developed with Gradio, which you can try on HuggingFace Spaces. You can also launch it locally:

python3 scripts/app.py

Contribution and Feedback

We welcome all forms of contribution, including but not limited to:

• Submitting issue reports
• Suggesting improvements
• Submitting code changes (please open an issue for discussion first)

Please contact us via the project's issues.

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License

This project is open-sourced under the Apache 2.0 license. You are free to use, modify, and distribute the code of this project under the terms of the license.