Feature Extraction and Classification Schemes for Enhancing Amharic Braille Recognition System
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Date
2011-06
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Addis Ababa University
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.
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Amharic Braille Recognition System