Handwritten and Machine printed OCR for Geez Numbers Using Artificial Neural Network
This work addresses a gap in OCR for the Geez number system, which is important for digitizing historical or cultural documents in languages using Ethiopic scripts, but it is incremental as it applies an existing neural network method to a new dataset.
The paper tackled the recognition of handwritten and machine-printed Geez numbers, which had been excluded from prior studies, using a feed-forward back-propagation artificial neural network and achieved an overall classification accuracy of approximately 89.88%.
Researches have been done on Ethiopic scripts. However studies excluded the Geez numbers from the studies because of different reasons. This paper presents offline handwritten and machine printed Geez number recognition using feed forward back propagation artificial neural network. On this study, different Geez image characters were collected from google image search and three persons are instructed to write the numbers using pencil. In total we have collected 560 numbers of characters. We have used 460 of the characters for training and 100 are used for testing. Accordingly we have achieved overall all classification ~89:88%