A Study On Online Handwriting Recognition For Ethiopic Characters: The Case Of Writer Independent Online Handwriting Recognition For Ethiopic Characters. Cet article n’est pas disponible.A Study On Online Handwriting Recognition For Ethiopic Characters: The Case Of Writer Independent Online Handwriting Recognition For Ethiopic Characters

Nowadays, online handwriting systems is one of the most common ways of interacting with hand held computing devices. Despite this fact, the absence of localized pen computing applications for Ethiopic script calls for the development and implementation of a writer-independent online handwriting recognition system for Ethiopic characters. Therefore, in this study the first prototype to ever be developed for a writer independent online handwriting recognition system for Ethiopic characters is demonstrated. The design of this prototype has concentrated on the thirty four first order Ethiopic characters with the vision of expanding the data corpus to include all Ethiopic characters and numerals. The data was collected from thirty randomly chosen individuals using a Wacom pen and tablet digitizer. Neuroscript MovaAlyzer was the software used to sample data points. The matching algorithm is Dynamic Time Warping. This algorithm calculated the distances between characters in the database and has yielded a recognition accuracy of 86.9%. With the expansion of the data corpus this recognition rate has promising prospects ahead.

Nowadays, online handwriting systems is one of the most common ways of interacting with hand held computing devices. Despite this fact, the absence of localized pen computing applications for Ethiopic script calls for the development and implementation of a writer-independent online handwriting recognition system for Ethiopic characters. Therefore, in this study the first prototype to ever be developed for a writer independent online handwriting recognition system for Ethiopic characters is demonstrated. The design of this prototype has concentrated on the thirty four first order Ethiopic characters with the vision of expanding the data corpus to include all Ethiopic characters and numerals. The data was collected from thirty randomly chosen individuals using a Wacom pen and tablet digitizer. Neuroscript MovaAlyzer was the software used to sample data points. The matching algorithm is Dynamic Time Warping. This algorithm calculated the distances between characters in the database and has yielded a recognition accuracy of 86.9%. With the expansion of the data corpus this recognition rate has promising prospects ahead.