Gondere, Mesay Samuel and Schmidt-Thieme, Lars and Boltena, Abiot Sinamo and Jomaa, Hadi Samer - Archives of Data Science, Series A

Article Details

Title Handwritten Amharic Character Recognition Using a Convolutional Neural Network
Authors Gondere, Mesay Samuel and Schmidt-Thieme, Lars and Boltena, Abiot Sinamo and Jomaa, Hadi Samer
Year 2020
Volume 6(1)
Abstract 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.