Arabic Handwritten Letters Recognition
| dc.contributor.author | Lamri, Hamza | |
| dc.contributor.author | Laidoune, Zakaria | |
| dc.contributor.author | Supervisor: Bentrcia, Rahima | |
| dc.date.accessioned | 2022-07-20T13:16:57Z | |
| dc.date.available | 2022-07-20T13:16:57Z | |
| dc.date.issued | 2022-06-10 | |
| dc.description.abstract | The main goal of our work is to exploit the effectiveness of artificial intelligence methods to recognize handwritten Arabic letters. In this work, for the dataset, we use the AHCD dataset to train and test the Random Forest model. We rely on two different approaches to recognize the letter. In the first one, the model takes as inputs 16 features extracted from the image, whereas in the second approach, the model takes the whole image as an input. We use Python as the programming language. We achieved a training accuracy of 97.14% and a testing accuracy of 73.48%. | en_US |
| dc.identifier.uri | http://dspace.univ-msila.dz:8080//xmlui/handle/123456789/30860 | |
| dc.language.iso | en | en_US |
| dc.publisher | UNIVERSITY of M'SILA | en_US |
| dc.subject | Artificial Intelligence, Random Forest, feature extraction, recognize handwritten Arabic letters, Python. | en_US |
| dc.title | Arabic Handwritten Letters Recognition | en_US |
| dc.type | Thesis | en_US |