- Tytuł:
- Deep convolutional neural network using a new data set for berber language
- Autorzy:
-
Mokrane, Kemiche
Sadou, Malika - Tematy:
-
optical character recognition
handwritten character recognition
CNN
Berber-MNIST data set
EMNIST
Tifinagh
Latin characters - Pokaż więcej
- Wydawca:
- Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
- Powiązania:
- https://bibliotekanauki.pl/articles/27312869.pdf Link otwiera się w nowym oknie
- Opis:
- Currently, handwritten character recognition (HCR) technology has become an interesting and immensely useful technology; it has been explored with impressive performance in many languages. However, few HCR systems have been proposed for the Amazigh (Berber) language. Furthermore, the validation of any Amazigh handwritten character-recognition system remains a major challenge due to the lack of availability of a robust Amazigh database. To address this problem, we first created two new data sets for Tifinagh and Amazigh Latin characters by extending the well-known EMNIST database with the Amazigh alphabet. Then, we proposed a handwritten character recognition system that is based on a deep convolutional neural network to validate the created data sets. The proposed convolutional neural network (CNN) has been trained and tested on our created data sets, the experimental tests showed that it achieves satisfactory results in terms of accuracy and recognition efficiency.
- Dostawca treści:
- Biblioteka Nauki
Artykuł