Gaussian Mixture Models for Arabic Font Recognition
| Art der Publikation: | Artikel in einem Konferenzbericht |
| Zitat: | fouad10:icpr |
| Buchtitel: | 20th International Conference on Pattern Recognition (ICPR 2010) |
| Jahr: | 2010 |
| Monat: | August |
| Seiten: | 2174-2177 |
| Ort: | Istanbul (Turkey) |
| DOI: | 10.1109/ICPR.2010.532 |
| Abriss: | We present in this paper a new approach for Arabic font recognition. Our proposal is to use a fixed- length sliding window for the feature extraction and to model feature distributions with Gaussian Mixture Models (GMMs). This approach presents a double advantage. First, we do not need to perform a priori segmentation into characters, which is a difficult task for arabic text. Second, we use versatile and powerful GMMs able to model finely distributions of features in large multi-dimensional input spaces. We report on the evaluation of our system on the APTI (Arabic Printed Text Image) database using 10 different fonts and 10 font sizes. Considering the variability of the different font shapes and the fact that our system is independent of the font size, the obtained results are convincing and compare well with competing systems. |
| Schlagworte: | arabic, GMM, machine learning, OCR |
| Autoren | |
| Hinzugefügt von: | [] |
| Gesamtbewertung: | 0 |
|
Anhänge
|
|
|
|
|
Notizen
|
|
|
|
|
|
Themen
|
|
|
|
|
