Online Handwriting Recognition for Ethiopic Characters
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Date
2005-06
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Addis Ababa University
Abstract
A new computing scheme, pen computing, which includes mobile devices and applications in
which electronic pen along with pen sensitive writing pad is used as the main input tool has
been emerging. To implement pen-computing applications, online handwriting recognition
system should be used. Online handwriting recognition engines have been developed for
various character sets. Despite that, no attempt has ever been made to build an online
handwriting recognition engine for Ethiopic character set. Pen-based inputting incorporated
with online handwriting recognition feature allows people to write texts and enter input data in
their own natural way of handwriting on an electronic pad.
This thesis then is the first attempt to develop an online handwriting character recognition
engine for Ethiopic characters. The pen-based devices are evidently unusual in Ethiopia and
one reason for that is the absence of localized applications. Bringing an online handwriting
recognition engine for Ethiopic character set to such devices would play an important role in
making these devices available and usable for the Ethiopian society.
In this study, a model for Ethiopic online handwriting character recognition is proposed and a
writer-dependent online handwriting character recognition engine for the 33+1 basic Ethiopic
characters is designed. The designed engine integrates five modules: the data collection and
preparation module, the preprocessing module, the feature extraction module, the training
module and the classification module. Data collection is done with the aid of digitizer
software named Neuroscript MovAlyzer, which samples data points along the trajectory of an
input device (electronic pen or mouse) while the character is drawn. Various algorithms are
designed for the preprocessing activities. In the feature extraction module, a new online
handwriting data representation scheme that makes use of the X and Y coordinate observation
code sequences is proposed. A training algorithm and most importantly a three-layered
recognizer is designed. We are able to show that a reasonably good accuracy is obtained by
implementing the proposed algorithms. On the average, a recognition accuracy of up to 99.4%
is achieved for the sampled two writers. Recognition accuracy 93.4%, 99%, 99.8% are also
obtained for each of the layers of the recognizer respectively.
Keywords: Online handwriting recognition, Online handwriting recognition for Ethiopic
Characters, algorithms for Ethiopic online handwriting recognition, Model for
Ethiopic character online handwriting recognition.
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Keywords
Online Handwriting Recognition; Online Handwriting Recognition for Ethiopic Characters; Algorithms for Ethiopic Online Handwriting Recognition; Model for Ethiopic Character Online Handwriting Recognition