How to Use Artificial Intelligence to Transform Web Accessibility
Introduction :
Artificial intelligence (AI) and machine learning technologies are used in a variety of industries. These technologies are causing actual change and assisting firms in optimizing their operations. As a result, the AI market is predicted to reach a remarkable $15.7 trillion by 2030. Significant progress has also been made in terms of how AI may be used to improve web accessibility. Read on to find out how your company might benefit from employing AI based accessibility testing.
How AI aids Web Accessibility?
With the rising incorporation of AI in digital products, there was an early recognition of the need to use it to help accessibility testing. The greatest promise of AI was thought to be a well-trained program’s ability to recognize data and provide several solutions to any given situation. When the same program is supplied with user accessibility criteria and is perfectly educated for inclusiveness, it can service end users’ more sophisticated accessibility needs. AI has the ability to address a number of other issues that impaired users experience.
Aside from that, it requires time to analyze a massive quantity of accessibility needs and extract feasible solutions using human capacity. However, AI’s powerful algorithms are capable of handling this with remarkable speed and accuracy. AI and accessibility serve as a support system by removing barriers, simplifying user activities, and providing an alternative method for completing challenging tasks. For example, facial recognition is an AI-powered alternative to inputting passwords that is intended to help visually impaired users or those with wounded limbs.
AI-Powered Web Accessibility Solutions
Some AI-based solutions that help with web accessibility are listed below.
Image Recognition- Images are an essential component of the internet. Every online application makes use of graphics to improve communication and content presentation. A page with images, on the other hand, is extremely comparable to a blank page for a visually impaired user. To address this, tech giants began to employ picture recognition software. Image recognition is a sort of computer vision AI that uses an artificial alternative text feature to dynamically describe images.
Neural networks and image processing algorithms can assist in identifying, categorizing, associating, and indexing things of interest within a picture. Image recognition compares an image encountered in a web application to millions of pre-indexed images to provide dynamic descriptions. These descriptions are used by screen readers. With breakthroughs in pre-indexed image data, upgrading methodologies, and practical algorithms, the precision of the conclusion is likewise increasing.
Facial Recognition- Authentication is required to access any secure web application. To ensure authentication, most users must enter a password, PIN, or pass a CAPTCHA test. However, handling the authentication process is difficult for visually impaired users. Facial recognition is a type of biometric security that identifies a person’s face using a face analyzer feature. The system measures facial traits from various angles and analyzes data from many photographs of a person’s face.
This technology learns from past encounters and makes the correct assumptions when it comes to recognizing the person in front of the camera. Facial recognition helps visually impaired users by simplifying authentication and removing CAPTCHA. There is no need to perform a CAPTCHA test once the program detects that a person engaging with it is a human through the camera lens.
Speech Recognition- Users with physical, cognitive, and developmental disabilities who prefer speaking to typing will like the speech recognition capability. It is a sort of assistive technology that assists users with tasks such as sending an email, placing an order, filling out a form, scrolling through pages, looking for a product, starting a call, and dictating text to type.
Natural Language Processing (NLP) and Machine Learning (ML) are used by speech recognition software to recognize, understand, and translate a user’s speech into text. It gets an electrical signal from a user’s speech and converts it to a digital signal by removing the noise factor. The speech recognition program then converts the digital signals into phonemes, which are then converted into accessible text.
This capability is also employed in the development of auto-captioning video content using ASR (Automated Speech Recognition) and AV-ASR (Audio Visual – Automatic Speech Recognition) technologies. This tremendously benefits hearing-impaired users as well as those who have accent or language understanding issues.
Text Recognition – Screen readers or text-to-speech software are used by users with visual and learning disabilities to read web information aloud in their preferred voice and speed. When it comes to photos, PDFs, and documents, there is an additional capability that allows you to read the material. Text recognition is a feature. Text recognition, commonly known as Optical Character Recognition (OCR), is the process of extracting data from scanned photos, documents, and PDFs and converting it into readable text. This feature operates by storing various character patterns as templates and employing pattern-matching algorithms.
Before transforming the text image to readable text, these algorithms examine and compare it to the recorded patterns. Intelligent Character Recognition (ICR), which interprets the text as a computer program, is one example of this feature’s advancement. The system examines visual properties such as curves, formats, hues, and lines and processes data at several levels. Intelligent Word Recognition (IWR) is similar to ICR, except it processes the entire word. Similarly, Optical Mark Recognition (OMR) identifies the symbols, logos, and watermarks in the document.
AI’s Limitations
The growing need for AI-powered accessibility tools has resulted in a slew of improvements to AI-based solutions in this space. However, there are still significant constraints such as accuracy, bias, security, sensitivity, and responsibility. Furthermore, solutions that focus on a single sort of accessibility need will eventually exclude people with various disabilities. Face recognition solutions, for example, may be difficult for autistic users, whilst voice recognition solutions will not be usable for vocally handicapped users.
Some AI-based solutions give misleading or erroneous results, which is why they have yet to acquire widespread adoption among end users. For example, auto-generated descriptions or image alt text are sometimes less accurate than a human expert’s output, and descriptions for complex photos are occasionally wrong. AI can potentially make biased judgements, causing concerns with user experience. For example, AI may determine that a color contrast option is appropriate based on the majority of users even though it causes problems for a color-blind user.
The most serious concern with AI-based products is their security. AI-based technologies have access to all sensitive user information for which they are not responsible. Appropriate security measures must be implemented to protect users and their data.
The future of AI in Web Accessibility
Though many AI-powered accessibility solutions are still in their early phases and have limits, their ability to improve and increase capabilities is undeniable. Every successful technology today had a difficult beginning. AI is a burgeoning discipline that is more advanced than all existing technology. Massive research and development is taking place around the world to build the most powerful AI-based solutions for web accessibility, solving current gaps and giving an amazing user experience. This is only the beginning. More breakthroughs are almost certainly on the way.