MACHINE LEARNING FOR BRAILLE CONTRACTION PREDICTION
Abstract
Machine learning for braille contraction prediction refers to the use of artificial intelligence techniques to predict appropriate contractions in braille documents. Braille contractions are shortened forms of words or word combinations that are commonly used to save space on the page. However, the use of contractions can be difficult to master, especially for novice braille readers. By using machine learning algorithms, it's possible to analyze patterns in large amounts of braille text to identify commonly used contractions and predict when they are appropriate to use. This can aid in making braille documents more readable and efficient by reducing the amount of space needed to represent the text. Additionally, it allows to make the learning process of braille reading more accessible, since the novices can rely on the help of the algorithm. Some specific tasks include the use of language models to predict the next word in a sentence based on context, or image recognition to identify objects in images and represent them with the corresponding braille signs. This machine learning based methods have been proven to be effective in increasing the readability and efficiency of braille documents, and can have a positive impact on the literacy and independence of visually impaired individuals