Ancient Ethiopic Manuscript Recognition Using Deep Learning Artificial Neural Network
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Date
2016-03
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Addis Ababa University
Abstract
The recognition of handwritten documents, which aims at transforming written
text into machine encoded text, is considered as one of the most challenging
problems in the area of pattern recognition and an open research area. Especially
ancient manuscripts, like Ethiopic Geez scripts, are different from the
modern documents in various ways such as writing style, morphological structure,
writing materials and so on. This brings the necessity to make research
works on characetr recogntion of those scripts. Geez is one of the ancient languages
which has been used as a liturgical language in Ethiopia. Manuscripts
written using this language contains many unexplored content which is the
base of the current Ethiopic scripts; however, only few researches have been
done on these valuable documents.
A number of algorithms have been proposed for handwritten character recognition
such as support vector machine, hidden Markov model, and neural network.
In this research the design and implementation of character recognition
system for ancient Ethiopic manuscript using deep neural network is presented.
Deep learning, is employed and trained using a Restricted Boltzman
Machine (RBM), a greedy layer-wise unsupervised training strategy.
The complete system employs image acquisition, preprocessing, character segmentation,
and classification and recognition. Efficient and effective algorithms
were selected and implemented in each step. A dataset was also prepared to
train and test the system, which consists of 24 base characters of Geez alphabet
with 100 frequencies. Overall, a recognition accuracy of 93.75 percent was
obtained using 3 hidden layers with 300 neurons. Analysis of results obtained
i
from each step of the recognition process shows that the system can be extended
and fine-tuned for practical application.
Key words: Ancient Ethiopic Manuscript, Handwritten Recognition, Preprocessing,
segmentation, Deep Neural Network, Restricted Boltzmann Machine.
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Keywords
Ancient Ethiopic Manuscript, Handwritten Recognition, Preprocessing, Segmentation, Deep Neural Network, Restricted Boltzmann Machine