Handwritten Amharic Character Recognition System Using Convolutional Neural Networks

Year 2019, Volume: 14 Issue: 2, 71 - 87, 20.04.2019

Fetulhak Abdurahman

Abstract

       Amharic
language is an official language of the federal government of the Federal
Democratic Republic of Ethiopia. Accordingly, there is a bulk of handwritten
Amharic documents available in libraries, information centres, museums, and
offices. Digitization of these documents enables to harness already available
language technologies to local information needs and developments. Converting
these documents will have a lot of advantages including (i) to preserve and
transfer history of the country (ii) to save storage space (ii) proper handling
of documents (iv) enhance retrieval of information through internet and other
applications. Handwritten Amharic character recognition system becomes a
challenging task due to inconsistency of a writer, variability in writing
styles of different writers, relatively large number of characters of the
script, high interclass similarity, structural complexity and degradation of
documents due to different reasons. In order to recognize handwritten Amharic
character a novel method based on deep neural networks is used which has
recently shown exceptional performance in various pattern recognition and
machine learning applications, but has not been endeavoured for Ethiopic
script. The Convolutional neural network model is evaluated for its performance
using our database that contains 132,500 datasets of handwritten Amharic
characters. Common handwritten recognition systems using machine learning use a
combination of both feature extractors and classifiers. Currently the use of
deep learning techniques shows promising improvements for machine learning
based classification tasks. Our proposed CNN model gives an accuracy of 91.83%
on training data and 90.47% on validation data.

Keywords

Amharic, Handwritten, Character, Convolutional neural network, Recognition, Recognition

HANDWRITTEN AMHARIC CHARACTER RECOGNITION SYSTEM USING CONVOLUTIONAL NEURAL NETWORKS

Abstract

Amharic language is an official language of the federal government of the Federal Democratic Republic of Ethiopia. Accordingly, there is a bulk of handwritten Amharic documents available in libraries, information centres, museums, and offices. Digitization of these documents enables to harness already available language technologies to local information needs and developments. Converting these documents will have a lot of advantages including (i) to preserve and transfer history of the country (ii) to save storage space (ii) proper handling of documents (iv) enhance retrieval of information through internet and other applications. Handwritten Amharic character recognition system becomes a challenging task due to inconsistency of a writer, variability in writing styles of different writers, relatively large number of characters of the script, high interclass similarity, structural complexity and degradation of documents due to different reasons. In order to recognize handwritten Amharic character a novel method based on deep neural networks is used which has recently shown exceptional performance in various pattern recognition and machine learning applications, but has not been endeavoured for Ethiopic script. The Convolutional neural network model is evaluated for its performance using our database that contains 132,500 datasets of handwritten Amharic characters. Common handwritten recognition systems using machine learning use a combination of both feature extractors and classifiers. Currently the use of deep learning techniques shows promising improvements for machine learning based classification tasks. Our proposed CNN model gives an accuracy of 91.83% on training data and 90.47% on validation data. 

Keywords

Amharic, Handwritten, Character, Convolutional neural network, Recognition

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