Title: Online Handwriting Recognition for Ethiopic Characters


Advisor: Dr. Solomon Atnafu

Keywords: Online handwriting recognition, Online handwriting recognition for Ethiopic, algorithms for Ethiopic online handwriting recognition, Model for

Copyright: 2005

Date Added: 28-Aug-2008

Publisher: 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.

Description: A Thesis submitted to the School of Graduates Studies of Addis Ababa University in partial fulfillment of the requirements for the degree Master of Science in Computer Science


Appears in: Thesis - Computer Science