An Algorithm for Automatically Detecting Dyslexia on the Fly

There are different types of algorithms used in eye tracking technologies. These algorithms are divided into two main categories: feature-based and model-based. Feature-based technologies consist of threshold values, which are used to decide the presence or absence of features or determinant factors. While the model-based approach is an iterative search of a model parameter, which is the best fitting model that is a closest match to the image. However, these approaches have significant problems regarding computational speed and accuracy.

Similarly, there are different types of eye – tracking technologies, which depend on different types of technologies such as infrared video cameras and other technologies, which require specific calibration and setup and are quite expensive. Therefore, in this paper, we propose an alternative eye–tracking technology using a new eye-tracking algorithm, which is highly portable and independent of any hardware or software systems. In an evaluation the algorithm worked accurately for users with strong dyslexia. Participants had various positive and negative opinions regarding such an auto-detection system. Furthermore, we propose that such technology could be used to automatically modify the content of online material to better suit dyslexic users.