Recognition of arabic handwritten letters using deep Learning approach
| dc.contributor.author | Chiekhaoui, Marouane | |
| dc.contributor.author | Ali, aichouche | |
| dc.contributor.author | Bentrcia, Rahima: Supervisor | |
| dc.date.accessioned | 2024-07-14T08:54:06Z | |
| dc.date.available | 2024-07-14T08:54:06Z | |
| dc.date.issued | 2023-06 | |
| dc.description.abstract | Many languages have made significant advancements in the field of character recognition, including English, Chinese, Japanese, and French, with recognition rates reaching up to 100% in some cases. However, Arabic handwriting recognition faces lower recognition rates, primarily due to certain linguistic characteristics that make the recognition process more challenging, along with a shortage of high-quality available datasets. Therefore, this memorandum was undertaken with the aim of developing a system for recognizing handwritten Arabic characters and a word segmentation system. The study began with an analysis of the Arabic language's structure, followed by an overview of deep neural network technology, which has proven its efficiency in achieving rapid and reliable recognition results. Finally, the obtained results were explained and interpreted. | |
| dc.identifier.uri | https://dspace.univ-msila.dz/handle/123456789/43657 | |
| dc.language.iso | en | |
| dc.publisher | MOHAMED BOUDIAF UNIVERSITY - M'SILA | |
| dc.subject | recognition of handwritten Arabic letters | |
| dc.subject | segmentation of handwritten Arabic words | |
| dc.subject | convolutional neural networks | |
| dc.subject | processing | |
| dc.subject | Feature extraction | |
| dc.subject | classification | |
| dc.title | Recognition of arabic handwritten letters using deep Learning approach | |
| dc.type | Thesis |