AI-Powered Image Alt Text Generation: Tools and Best Practices

Alt text is the backbone of image accessibility on the web. Without it, screen reader users encounter blank spots where sighted users see photos, charts, and illustrations. WCAG 2.2 Success Criterion 1.1.1 requires that all non-decorative images include text alternatives. The challenge: billions of images exist online, and the vast majority lack adequate descriptions.

AI-powered alt text generators are narrowing that gap by using computer vision models to analyze images and produce descriptive text automatically. The question is not whether these tools work, but how well they work and where human oversight remains essential.

How AI Alt Text Generation Works

Modern alt text generators use deep learning models, typically convolutional neural networks combined with transformer-based language models, to process an image and generate a natural language description. The pipeline generally follows three steps:

Object detection identifies discrete elements in the image (people, furniture, animals, text).
Scene understanding interprets the relationship between objects (a person sitting at a desk, a dog running through a park).
Caption generation produces a grammatically correct sentence describing the scene.

Models like those behind Microsoft Azure Computer Vision, Google Cloud Vision, and Meta’s automatic alt text system are trained on millions of image-caption pairs scraped from the web and curated datasets like COCO (Common Objects in Context).

Leading Tools

Platform-Native Solutions

Facebook/Meta automatically generates alt text for uploaded images, describing detected objects (e.g., “may contain: 2 people, smiling, outdoor”).
Microsoft Office offers automatic alt text in Word, PowerPoint, and Outlook, using Azure AI.
Google Photos generates descriptions for images, feeding into Google’s accessibility features.

Standalone and API Tools

Microsoft Azure Computer Vision API provides detailed image descriptions, object detection, and OCR in a single call.
Google Cloud Vision API offers label detection, text extraction, and safe-search annotation.
Clarifai provides custom model training alongside pre-built recognition models.
Be My Eyes uses GPT-4-powered vision to generate rich, contextual descriptions on demand for blind and low-vision users.

CMS Integrations

WordPress plugins like Flavor, Jepto Accessibility, and the WordPress Accessibility Plugin can auto-generate alt text on image upload. Shopify and other e-commerce platforms are adding similar features for product images.

What AI Gets Right

AI alt text excels at:

Identifying common objects. Chairs, cars, food items, animals, and standard scenes are recognized with high reliability.
Reading embedded text. OCR capabilities extract text from signs, labels, and documents within images.
Scaling. An API call takes milliseconds. Retroactively adding alt text to thousands of legacy images becomes feasible.
Consistency. AI produces descriptions in a uniform format, avoiding the wild variation in quality that manual alt text often shows.

Where AI Falls Short

AI alt text struggles with:

Context and purpose. A photo of a handshake might illustrate a business partnership, a peace agreement, or a personal greeting. The AI sees “two people shaking hands” but cannot infer the editorial context.
Complex or abstract images. Charts, infographics, editorial illustrations, and art require understanding that goes beyond object detection.
Cultural specificity. Traditional clothing, regional foods, cultural ceremonies, and non-Western architecture may be misidentified or described generically.
People description. AI intentionally avoids identifying race, gender, or age in most contexts, which can result in vague descriptions. It also cannot identify specific individuals unless specifically trained (which raises privacy concerns).
Decorative vs. functional images. AI cannot determine whether an image is decorative (requiring null alt text) or informative without understanding the surrounding content.

Best Practices for AI-Assisted Alt Text

Use AI as a first draft, not a final answer. Generate alt text automatically, then review and edit. Prioritize manual review for hero images, product photos, charts, and any image carrying editorial weight.
Add context the AI cannot infer. If a photo accompanies an article about climate change, ensure the alt text connects the image to that topic rather than generically describing “an aerial view of a coastline.”
Set quality thresholds. Flag AI-generated descriptions that are too vague (“an image of a room”) for human review.
Handle decorative images separately. Mark purely decorative images with empty alt attributes (alt="") rather than generating unnecessary descriptions.
Test with screen reader users. The ultimate measure of alt text quality is whether it gives a screen reader user the information they need to understand the content.

For automated compliance checking of alt text and other WCAG criteria, see automated WCAG compliance checking with AI. To understand how these tools extend to social media, read AI image description for social media.

The Accuracy Question

Studies consistently show that AI-generated alt text is more useful than no alt text at all, and often comparable to low-quality human-written descriptions. However, expert-written alt text that considers editorial context still outperforms AI in most evaluations.

The practical recommendation: use AI to eliminate the backlog of undescribed images (the vast majority of web images), and invest human effort where it matters most.

Key Takeaways

AI alt text generators use computer vision and language models to describe images automatically, addressing the massive scale problem of undescribed web content.
Leading platforms (Microsoft, Google, Meta) have built alt text generation into their products, and standalone APIs enable custom implementations.
AI handles common objects and text extraction well but struggles with context, abstract imagery, and culturally specific content.
The most effective workflow uses AI for first-draft generation with human review for high-priority images.
No AI tool currently replaces the need for editorial judgment about what an image communicates in its specific context.

Sources

WCAG 2.2 Success Criterion 1.1.1 — non-text content requirements: https://www.w3.org/WAI/WCAG22/Understanding/non-text-content.html
Microsoft Azure Computer Vision API — image analysis and description: https://azure.microsoft.com/en-us/products/ai-services/ai-vision
Be My Eyes — AI-powered visual assistance platform: https://www.bemyeyes.com/
COCO dataset — common objects in context for training vision models: https://cocodataset.org/