How Do I Check Alt Text Quality in My DAM Against WCAG 2.2?

The AI Alt Text Quality Checker accepts image alt text (and optional contextual notes about the image) and evaluates it against WCAG 2.2 Success Criterion 1.1.1 (Non-text Content, Level A), then returns a structured quality assessment and one or more suggested rewrites for human review. As the W3C Web Accessibility Initiative specifies in its WCAG 2.2 Understanding document for SC 1.1.1, the criterion requires that all non-text content presented to users has a text alternative serving an equivalent purpose, with defined exceptions for decorative content (empty alt attribute), CAPTCHA, and purely sensory material. TdR's editorial team validated the tool's assessment logic against these W3C-published exception categories during internal testing before release.

• WCAG 2.2 SC 1.1.1 alignment check: Flags alt text that fails or risks failing Success Criterion 1.1.1, including empty alt attributes on informational images and non-empty alt attributes on purely decorative images.
• Clarity and specificity scoring: Identifies descriptions that are too generic (for example, 'image of a person') and explains what additional detail would make them more useful to assistive technology users.
• Length and redundancy review: Highlights alt text that is excessively long, begins with redundant phrases such as 'image of' or 'photo of,' or duplicates surrounding caption text, all of which are common failure patterns under SC 1.1.1 guidance.
• Context-aware rewrite suggestions: Generates one or more alternative descriptions calibrated to the image type (informational, functional, decorative, or complex) as defined in WCAG 2.2 guidance.
• Batch input support: Accepts multiple alt text entries in a single session so teams can work through a queue of DAM records efficiently.
• Plain-language rationale: Accompanies every suggestion with a brief explanation of why the original text fell short and how the rewrite addresses the gap, supporting team learning over time.

What this does not cover: This tool does not assess contrast ratios, keyboard navigation, or any WCAG 2.2 criterion other than SC 1.1.1. It does not evaluate alt text rendered inside PDF documents or video captions, which involve separate accessibility standards. It cannot verify whether an image's surrounding page context changes the appropriate alt text treatment, a judgment that always requires human review. Finally, the tool does not assess compliance with jurisdiction-specific regulations beyond the WCAG 2.2 framework, such as Section 508 technical provisions or EN 301 549 (the European accessibility standard), which may impose additional or differing requirements.

Accessibility compliance is no longer optional for organizations that publish digital content, and alt text failures are among the most measurable and preventable gaps in any DAM library. According to WebAIM (2024), missing or inadequate image alt text has appeared in the top five most common WCAG Level A failures in every edition of its annual Million report since 2019, affecting millions of images across the web. The U.S. Department of Justice finalized its ADA Title II web accessibility rule in April 2024, a development described by Forbes (2024) as a historic milestone for digital accessibility, with WCAG 2.1 Level A and AA as the applicable technical standard. Because WCAG 2.2 is backward-compatible with WCAG 2.1, meeting WCAG 2.2 SC 1.1.1 satisfies the equivalent WCAG 2.1 requirement. In TdR's ongoing, vendor-neutral evaluation of the DAM market, alt text quality consistently emerges as a cross-platform metadata gap: no single vendor category has solved this problem at the library level, which is precisely why a platform-agnostic checker operating at the metadata layer is valuable regardless of which DAM system a team uses.

• Legal and regulatory risk reduction: Inadequate alt text on published images can constitute a failure to meet WCAG 2.2 Level A requirements (SC 1.1.1), creating exposure under the ADA and equivalent regulations in other jurisdictions.
• Improved assistive technology experience: Screen reader users depend on accurate alt text to understand visual content; poor descriptions create genuine barriers to information access that a populated-but-meaningless alt attribute does not solve.
• Metadata governance at scale: DAM libraries can contain tens of thousands of images. Manual alt text auditing is impractical without a structured tool to triage and prioritize remediation work.
• Content reuse confidence: When alt text is accurate and compliant, assets can be confidently reused across web, email, and document channels without per-channel remediation.
• Team capability building: The tool's plain-language rationale helps content and DAM teams internalize WCAG 2.2 SC 1.1.1 principles, reducing the volume of poor alt text created in the first place.
• Upstream quality control: Catching alt text problems at the DAM metadata level, before assets are published, is far less costly than remediating live pages after an accessibility audit or complaint.

• DAM administrators and librarians conducting metadata audits or onboarding legacy image libraries that lack compliant alt text.
• Digital accessibility specialists who need a fast first-pass triage tool before committing to full manual remediation of a DAM collection.
• Content strategists and editors responsible for publishing images to web or email channels and needing to verify alt text quality before assets go live.
• Marketing operations teams managing large volumes of campaign imagery across multiple channels and brand regions.
• Web and UX teams integrating DAM-sourced assets into CMS or design systems and needing to confirm accessibility compliance at the asset level.
• Compliance and legal teams seeking evidence of a structured, repeatable alt text review process as part of a broader accessibility program under the ADA or equivalent regulations.

1. Sign in to your TdR account and open the AI Alt Text Quality Checker from your member dashboard or this page.
2. Paste the existing alt text for one or more images into the input field. Optionally, add a brief contextual note describing the image type (informational, decorative, functional, or complex) and its intended publishing context.
3. Submit the entry and review the tool's structured assessment, which identifies specific WCAG 2.2 SC 1.1.1 issues and explains the reasoning behind each flag.
4. Read the suggested rewrites provided by the assistant. There may be more than one option, particularly for complex or ambiguous images.
5. Apply human editorial judgment: Select, adapt, or reject each suggestion. The tool's output is a draft only; you are responsible for the final alt text that enters your DAM record.
6. Update your DAM metadata record with the approved alt text, noting the review date and the WCAG 2.2 criterion addressed, to support future audit trails.
7. Repeat for the next batch of assets, working through your remediation queue systematically.

The TdR AI Alt Text Quality Checker is designed for responsible, human-in-the-loop use. Every suggestion it produces is a draft recommendation only: no output should be applied to a DAM record without review and approval by a qualified human. The tool operates under fair-usage limits (a maximum number of submissions per day per account) to ensure consistent availability for all TdR members and to discourage fully automated, unreviewed bulk processing. The assistant does not store, index, or retain any text or image data submitted during a session; your content is processed in-session only and is not used to train or update any model. Do not paste proprietary asset metadata, personal data, or confidential information into the tool beyond what is necessary to describe the image for alt text purposes.

• WCAG 2.2 for DAM Teams: A TdR primer on how the Web Content Accessibility Guidelines apply to image metadata governance and DAM workflows.
• Metadata Standards in Digital Asset Management: An overview of controlled vocabularies, schema design, and quality scoring for DAM metadata fields including alt text and captions.
• Accessibility Auditing Your DAM Library: A practical TdR guide to planning, prioritizing, and executing an accessibility remediation project across a large image collection.

Accessible alt text is a foundational element of responsible digital asset stewardship. The TdR AI Alt Text Quality Checker gives DAM practitioners a structured, WCAG 2.2 SC 1.1.1-aligned starting point for improving image descriptions at scale, while keeping human expertise and editorial judgment at the center of every decision. As the Forbes (2024) coverage of the DOJ final rule makes clear, organizations that publish digital content bear a real and ongoing obligation to make that content accessible, and a well-governed DAM is one of the most effective places to build that discipline from the ground up.