Historical Ethiopic handwritten document recognition using deep learning
by Fitehalew Ashagrie Demilew; Yaregal Tadesse Tessema; Gezahegn Mulusew Delele; Habtamu Asmare Sendeku
International Journal of Information Technology, Communications and Convergence (IJITCC), Vol. 4, No. 2, 2023

Abstract: Document analysis involves different step by step processes which are image acquisition, preprocessing, segmentation feature extraction, and classification. The process of historical document recognition is much harder than the default handwritten document recognition systems. In historical document recognition, the documents are highly degraded. In Ethiopia, a large number of historical documents can be found in monasteries, libraries, and museums which are written in Amharic languages. Documents that is as old as 1,000 years can be found in Ethiopia written in Amharic and Ge'ez languages. This paper intends on developing a document recognition system for the historical handwritten Amharic documents by mainly focusing on the preprocessing phases. A dataset is prepared which compromises the 230 Amharic alphabets and the dataset's frequency varies from 190-320 images per class. A total of 44,000 isolated characters is collected and split into training, testing, and validation set with the ratio of 7:2:1 respectively.

Online publication date: Fri, 11-Aug-2023

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