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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/3524

Title: FEATURE EXTRACTION AND CLASSIFICATION SCHEMES FOR ENHANCING AMHARIC BRAILLE RECOGNITION SYSTEM
Authors: SHUMET, TADESSE
Advisors: Dr. Million Meshesha
Keywords: Information science
Copyright: Jun-2011
Date Added: 30-Jul-2012
Publisher: AAU
Abstract: Information in written form plays an undeniably important role in our daily lives. Recording and using information encoded in symbolic form is essential. Visually impaired people face a distinct disadvantage in this respect. To address their information need, the most widely adopted writing convention among visually impaired people is Braille. Since its inception in 1829, significant developments have taken place in the production of Braille and Braille media as well as in the transcription of printed material into Braille. Braille is understandable by visually impaired people; however vision people need not be able to understand these codes. The need to understand Braille documents by vision society and the production of huge amounts of Braille documents motivated the development of OBR for different languages (such as English, Arabic, etc.) across the world. The development of OBR for Amharic Braille has been started in recent years. However, OBR for Amharic Braille is still an area that requires the contribution of many research works. In this study an attempt has been made in exploring feature extraction and classification techniques for Amharic Braille recognizer. To extract valid Braille dots from a Braille image and to group them into Braille cells, three feature extraction algorithms based on: fixed cell measures, horizontal and vertical projections, and grid construction are tested. The experimental result shows that feature extraction based on fixed cell measures performs well. To build classification models for prediction of Amharic characters from Braille cell representation J48 decision tree and the support vector machine (SVM) classifiers are investigated. Based on experimental results SVM outperforms decision tree classifier in predicting unseen extracted Braille features. The explored feature extraction and classification techniques are integrated to the Amharic OBR system and are tested on real life Braille documents, in which 90.67% accuracy, on the average, is registered. This shows a promising result to design an applicable system. Handling noisy real-life Braille documents is the future research direction that needs an integration of generic segmentation and noise removal techniques.
Description: A Thesis Submitted to the School of Graduate Studies of Addis Ababa University in Partial Fulfillment of the Requirements for the Degree of Master of Science in Information Science
URI: http://hdl.handle.net/123456789/3524
Appears in:Thesis - Information Science

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