FontLoom

FontLoom is an intelligent font recommendation system that helps designers and developers discover visually compatible fonts. It combines machine learning techniques with a web interface to recommend fonts based on similarity and clustering.

The project uses a Django-based ML backend and a Node.js + Express frontend, containerized using Docker for easy deployment.

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Features

• Smart font recommendation system
• Font similarity detection using machine learning
• Font clustering to group visually similar fonts
• User-friendly web interface
• Scalable backend architecture
• Docker-based deployment

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Machine Learning Models

FontLoom uses the following algorithms:

K-Nearest Neighbors (KNN) Used to recommend fonts similar to a selected font.

K-Means Clustering Groups fonts into clusters based on their features.

These models allow FontLoom to suggest fonts that maintain visual harmony in typography design.

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Project Architecture

The project consists of two main components.

1. Backend (MODEL)

Built with Django and responsible for handling machine learning logic.

MODEL/
│
├── FontLoom_model/
│   ├── views.py
│   ├── urls.py
│   └── settings.py
│
├── manage.py
├── requirement.txt
└── DockerFile.django

Responsibilities:

• ML model processing
• Recommendation API
• Font similarity computation

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2. Frontend (WEB)

Built using Node.js and Express.js.

WEB/
│
├── server.js
├── routes/
├── views/
├── public/
├── DockerFile.node
└── docker-compose.yaml

Responsibilities:

• User interface
• API requests
• Rendering font recommendations

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Tech Stack

Frontend

• Node.js
• Express.js
• HTML / CSS / JavaScript

Backend

• Django
• Python

Machine Learning

• Scikit-learn
• KNN Algorithm
• K-Means Clustering

Deployment

• Docker
• Docker Compose

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How It Works

1. The user selects or searches for a font.
2. The request is sent to the backend recommendation system.
3. The machine learning model analyzes font features.
4. Similar fonts are identified using KNN.
5. Fonts are grouped using K-Means clustering.
6. The system returns recommended fonts to the frontend.

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Installation

Clone the repository

git clone https://github.com/yourusername/fontloom.git
cd fontloom

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Run with Docker

docker-compose up --build

This will start both:

• Django ML backend
• Node.js frontend server

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Future Improvements

• Deep learning based font embeddings
• Font pair recommendation system
• User preference learning
• Google Fonts API integration
• Visual similarity detection using CNN