KIT | KIT-Bibliothek | Impressum | Datenschutz

Handwritten Amharic Character Recognition Using a Convolutional Neural Network

Gondere, Mesay Samuel; Schmidt-Thieme, Lars; Boltena, Abiot Sinamo; Jomaa, Hadi Samer


Amharic is the official language of the Federal Democratic Republic of Ethiopia. There are lots of historic Amharic and Ethiopic handwritten documents addressing various relevant issues including governance, science, religious, social rules, cultures and art works which are very rich indigenous knowledge. The Amharic language has its own alphabet derived from Ge’ez which is currently the liturgical language in Ethiopia. Handwritten character recognition for non Latin scripts like Amharic is not addressed especially using the advantages of state-of-the-art techniques. This research work designs for the first time a model for Amharic handwritten character recognition using a convolutional neural network. The dataset was organized from collected sample handwritten documents and data augmentation was applied for machine learning.

Verlagsausgabe §
DOI: 10.5445/KSP/1000098011/09
Veröffentlicht am 26.03.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 2363-9881
KITopen-ID: 1000130983
Erschienen in Archives of Data Science, Series A
Band 6
Heft 1
Seiten P09, 14 S. online
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
KITopen Landing Page