Deep convolutional neural network using a new data set for berber language

• Autorzy: Mokrane Kemiche , Sadou Malika

Identyfikatory

DOI

10.7494/csci.2023.24.2.4783

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

optical character recognition; handwritten character recognition; CNN; Berber-MNIST data set; EMNIST; Tifinagh; Latin characters

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2023
• Tom: T. 24 (2)
• Strony: 225--241
• Opis fizyczny: Bibliogr. 27 poz., rys., tab.

Twórcy

autor

Mokrane Kemiche

• Research Center for Amazigh Language and Culture, Algeria

autor

Sadou Malika

• Research Center for Amazigh Language and Culture, Algeria

Bibliografia

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