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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.
EN
A novel character recognition method, called a Neuro-Fuzzy system combined with Particle swarm optimization for Handwritten Character Recognition (NFPHCR), is proposed in this paper. The NFPHCR method integrates Recurrent Neural Network (RNN), Fuzzy Inference System (FIS), and Particle Swarm Optimization (PSO) algorithm to recognize handwritten characters. It employs the RNN to effectively extract oriented features of handwritten characters, and then, these features are applied to create the FIS. Finally, the FIS combined with the PSO algorithm can powerfully estimate similarity ratings between the recognized character and sampling characters in the character database. Experimental results demonstrate that the NFPHCR method achieves a satisfying recognition performance and outperforms other existing methods under considerations.
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