Machine Learning with JavaScript and Brain.js


In today's technology-driven world, machine learning is transforming our daily lives and changing how we engage with digital media. When combined with JavaScript, machine learning can significantly enhance user experience, especially in improving accessibility. One critical aspect of web design often overlooked is colour contrast, a factor that impacts usability for individuals with visual impairments.

This article will explore how machine learning and colour contrast can work together through JavaScript and Brain.js. By the end, you will understand how these technologies can foster inclusion and user satisfaction on the web.

What is Colour Contrast?

Colour contrast refers to the difference in brightness and hue between two elements, such as text and background colours. Proper contrast is crucial for readability and ensures that content is accessible to all users, regardless of their visual abilities.

The effectiveness of contrast can be quantified through specific formulas. For instance, the Web Content Accessibility Guidelines (WCAG) recommend a minimum contrast ratio of 4.5:1 for normal text. For large text, a ratio of 3:1 is acceptable. Achieving these ratios ensures that content is easily readable, even for people with colour blindness or low vision. For example, if the background is white (RGB: 255, 255, 255) and the text is black (RGB: 0, 0, 0), the contrast ratio would be 21:1, well above the minimum requirement.

By ensuring strong colour contrast, we empower everyone to interact with web content effectively, helping to create a more inclusive online environment.

The Role of Machine Learning

Machine learning involves creating algorithms that allow computers to learn from data and make informed predictions. In web design, this technology can provide functionalities like automatic colour contrast analysis, greatly enhancing accessibility.

For example, by utilizing machine learning models, developers can create applications that evaluate colour combinations for their contrast ratios in real-time. These models not only provide immediate feedback on colour selection but can also recommend alternative colour combinations that meet accessibility standards. Research suggests using machine learning can improve the accuracy of colour assessments by up to 80%, ensuring better adherence to guidelines.

Brain.js: An Introduction

Brain.js is an approachable JavaScript library that simplifies the implementation of machine learning. This library enables developers, even those with minimal machine learning background, to create simple neural networks for a variety of applications.

For instance, you can use Brain.js for projects related to pattern recognition and predictive analysis. As colour contrast becomes more crucial for web accessibility, Brain.js can help determine optimal colour combinations quickly and effectively.

Creating a Simple Colour Contrast Checker with JavaScript and Brain.js

Now, let's see how JavaScript and Brain.js can be utilized to create a basic colour contrast checker.

Step 1: Setting Up the Environment

First, ensure your development environment is ready. You will need the latest version of Node.js installed. You can then create a new JavaScript project as follows:

mkdir colour-contrast-checker

cd colour-contrast-checker

npm init -y

npm install brain.js

Step 2: Implementing Brain.js

In your project directory, generate an `index.js` file. This file will define a neural network that learns how to measure colour contrast.

<script>

const brain = require('brain.js');

const net = new brain.NeuralNetwork();

// Sample training data

const trainingData = [

{ input: { r1: 255, g1: 255, b1: 255, r2: 0, g2: 0, b2: 0 }, output: { contrast: 1 } },

{ input: { r1: 255, g1: 0, b1: 0, r2: 0, g2: 255, b2: 0 }, output: { contrast: 0.5 } },

// Add more training samples here

];

// Train the network

net.train(trainingData);

</script>

Step 3: Contrast Calculation

Next, you need to calculate the contrast ratio between two colours. The following function computes the relative luminance for the contrast ratio:

<script>

function getContrastRatio(color1, color2) {

const luminance1 = calculateLuminance(color1);

const luminance2 = calculateLuminance(color2);

return (Math.max(luminance1, luminance2) + 0.05) / (Math.min(luminance1, luminance2) + 0.05);

}

function calculateLuminance(color) {

const { r, g, b } = color;

return 0.2126 (r / 255) + 0.7152 (g / 255) + 0.0722 * (b / 255);

}

</script>

Step 4: Putting it All Together

Now combine the functionalities to evaluate colours in real time. This includes taking user inputs for colours and using the trained neural network to predict their contrast.

<script>

const exampleColor1 = { r: 255, g: 0, b: 0 }; // Red

const exampleColor2 = { r: 0, g: 0, b: 255 }; // Blue

const contrastRatio = getContrastRatio(exampleColor1, exampleColor2);

console.log(`Contrast Ratio: ${contrastRatio}`);

</script>