Words Autocompleting Using Nesting Matrices

Abstract

Since 1980's, word-prediction systems have been used as writing aids. They were used by people with physical incompetence to reduce the amount of effort needed to enter text, but later they were found to be also helpful to people with learning or language weakness, such as difficulties in speaking, spelling, or grammar. The purpose of this paper is to propose an auto-completing system built using multidimensional nesting matrices to build a database that store the previously learned words, and use that database to find all the words that starts with a given two letters. Where, it's taken 5.343 seconds to look for word which starts with existing first two letters, and it has taken 2.2568 seconds to look for word which start with two nonexistent letters in the database.