AI Image Tools

Alt text (the alt attribute on an <img> element) serves two distinct purposes. For accessibility, screen readers rely on alt text to describe images to users who are visually impaired. Without it, a screen reader will typically announce the file name — something like "IMG_3847.jpg" — which communicates nothing. Well-written alt text gives these users the same information a sighted visitor gets from seeing the image.

For search engine optimization, Google and other search engines cannot see images the way humans do. They read the alt text, file name, and surrounding content to understand what an image depicts. Images with descriptive, relevant alt text rank better in Google Image Search — a significant source of traffic for many content-driven websites. Google also uses alt text as a relevance signal for the page overall, meaning properly labelled images can lift the entire page's organic ranking for related queries.

From a legal standpoint, missing alt text can create accessibility liability in many jurisdictions. The Americans with Disabilities Act (ADA) and the Web Content Accessibility Guidelines (WCAG 2.1) both require meaningful text alternatives for non-decorative images on public-facing websites. Automated accessibility auditing tools like Lighthouse and axe flag missing alt attributes as errors that must be resolved.

Good alt text describes the content and function of an image concisely. It should answer the question: "If this image could not be displayed, what information would the user miss?" Aim for one or two sentences that capture the subject, action, and context relevant to the page.

Always review and refine the AI-generated alt text before publishing. The model describes what it sees in the image but does not know the business context, the name of a product, or specific people — information only you can supply. Use the AI output as a starting draft and edit it to add the relevant context for your page.

Image classification predicts the category or categories an image belongs to, along with a confidence score for each prediction. The classifier used here (Microsoft's ResNet-50) is trained on ImageNet's 1,000 categories — objects, animals, vehicles, food, scenes, and more. The output is a ranked list of the most likely categories and how confident the model is in each one.

Classification is useful when you need to understand or organize a large library of images quickly. Common use cases include: tagging images in a CMS automatically before editorial review, filtering stock photo libraries by content type, building searchable image databases, and checking that uploaded user images contain appropriate content before publishing. The confidence scores let you set a threshold — for example, only auto-tag if the top prediction exceeds 80% confidence.