Handling Accessibility in Clinch Talent

Creating career sites and landing pages that are accessible to people with disabilities is very important, and in some jurisdictions, it's the law.

The relevant guidelines are from a body called W3C and are available at  https://www.w3.org/WAI/intro/wcag. In addition, the European Union also has a comprehensive guide into the same guidelines at http://ec.europa.eu/ipg/standards/accessibility/wcag-20/index_en.htm. Note: both documents complement each other and the EU guides are applicable globally.

By default, the Clinch Talent platform publishes pages that are broadly compliant with these guidelines. However, the flexibility of the platform creates opportunities for users to publish incompatible content. For example, adding images to a page that have text embedded within the image. Or using a video that flashes more than three times a second and risks causing seizures among viewers.

In that way, there is a shared-responsibility model adopted, where from a technical perspective the platform will generate valid pages and the end-users ensure that only suitable content is added and published.


Images are a particular area of interest when designing accessible pages. It is important to provide text alternatives for any non-text content so that it can be changed into other forms people need, such as large print, Braille, speech, symbols or simpler language.

Within the page editor, when adding or editing image blocks, there is an "alternative description" field where images can be described for disabled users.

Additionally, images can be added within the text editor, and there is also an "image description" field where the image can be described. This works similar to the "alternative description" field within image blocks.

Machine Learning

Providing an alternative description for every image is a significant chore. To assist with this task and to improve "out-of-the-box" compliance, the Clinch Talent platform attempts to use Machine Learning to automatically determine what is in each image that is uploaded to the  Image Gallery on the platform.

When an image is used from the  Image Gallery the detected items in each image are provided by default as an "alternative text", as show below. 

If you are dissatisfied with the default, you can provide your own alternative description from within the page editor.

Note: Currently, images used within a text block (added using the text tool), do not use the machine learning defaults automatically.

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