Handwritten Arabic Characters Recognition Based on Wavelet Entropy and Neural Network
K. Daqrouq *
Department of Computer and Electrical Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
M. N. Ajour
Department of Computer and Electrical Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
A. Alkhateeb
Department of Computer and Electrical Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
A. Morfeq
Department of Computer and Electrical Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
A. Dobaie
Department of Computer and Electrical Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
M. Badarin
Department of Electronics and Communications Engineering, Philadelphia University, Jordan
A. Rihawe
Department of Electronics and Communications Engineering, Philadelphia University, Jordan
*Author to whom correspondence should be addressed.
Abstract
The presented work proposed a wavelet packet and Shannon entropy (SEWP) technique for handwritten Arabic characters recognition system. Entropy has been applied in many applications. However, the combination of Shannon entropy with wavelet transform (WT) is proposed in this study of handwritten Arabic characters recognition. The investigation procedure was based on feature extraction and classification. For feature extraction, the distinguished features of handwritten Arabic characters were extracted using the SEWP technique. And for classification, probabilistic neural network (PNN) was applied because of its better performance and speedy processing. In the experimental investigation, the quality of wavelet transform in conjunction with Shannon entropy were studied. In addition, the capability analysis on the proposed system was studied by comparing with other systems. In response to our experimental results, the PNN classifier achieved a better recognition rate with SEWP as a feature extraction method.
Keywords: Arabic characters, shannon entropy, wavelet, probabilistic neural network.