Ancient Ethiopic Manuscript Recognition Using Deep Learning Artificial Neural Network

Endalamaw, Siranesh Getu (2016) Ancient Ethiopic Manuscript Recognition Using Deep Learning Artificial Neural Network. Masters thesis, Addis Ababa University.

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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 from each step of the recognition process shows that the system can be extended and fine-tuned for practical application.

Item Type: Thesis (Masters)
Uncontrolled Keywords: Ancient Ethiopic Manuscript, Handwritten Recognition, Preprocessing, segmentation, Deep Neural Network, Restricted Boltzmann Machine.
Subjects: C Auxiliary Sciences of History > CN Inscriptions. Epigraphy.
Q Science > Q Science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > T Technology (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Africana
Depositing User: Andriamparany Edilbert RANOARIVONY
Date Deposited: 19 Jul 2018 14:19
Last Modified: 19 Jul 2018 14:19

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