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ISSN: 2165- 7866

Journal of Information Technology & Software Engineering
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Research Article

Enhancing the Text Production and Assisting Disable Users in Developing Word Prediction and Completion in Afan Oromo

Workineh Tesema1* and Duresa Tamirat2

1Department of Information Science, Jimma University, Jimma, Ethiopia

2Department of Information Science, Medawolabu University, Robe, Ethiopia

*Corresponding Author:
Workineh Tesema
Department of Information Science
Jimma University, Jimma
378, Ethiopia
Tel: +251910127829
E-mail: [email protected]

Received Date: April 13, 2017; Accepted Date: April 21, 2017; Published Date: April 27, 2017

Citation: Tesema W, Tamirat D (2017) Enhancing the Text Production and Assisting Disable Users in Developing Word Prediction and Completion in Afan Oromo. J Inform Tech Softw Eng 7: 200. doi: 10.4172/2165-7866.1000200

Copyright: © 2017 Tesema W, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

This work presents a word prediction and completion for disable users. The idea behind this work is to open a chance to interact with computer software and file editing for disable users in their mother tongue languages. Like normal persons, disable users are also needs to access technology in their life. In order to develop the model we have used unsupervised machine learning. The algorithm that used in this work was N-grams algorithms (Unigram, Bigram and Trigram) for auto completing a word by predicting a correct word in a sentence which saves time, reduces misspelling, keystrokes of typing and assisting disables. This work describes how we improve word entry information, through word prediction, as an assistive technology for people with motion impairment using the regular keyboard, to eliminate the overhead needed for the learning process. We also present evaluation metrics to compare different models being used in our work. The result argued that prediction yields an accuracy of 90% in unsupervised machine learning approach. This work particularly helps disable users who have poor spelling knowledge or printing press, institutions or government organizations, repetitive stress injuries to their (wrist, hand and arm) but it needs more further investigation for users who have visual problems.

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