In this paper we explore a new model focused on integrating two classifiers; Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for offline Arabic handwriting recognition (OAHR) on which the dropout technique was applied. The suggested system altered the trainable classifier of the CNN by the SVM classifier. A convolutional network is beneficial for extracting features information and SVM functions as a recognizer. It was found that this model both automatically extracts features from the raw images and performs classification. Additionally, we protected our model against over-fitting due to the powerful performance of dropout. In this work, the recognition on the handwritten Arabic characters was evaluated; the training and test sets were taken from the HACDB and IFN/ENIT databases. Simulation results proved that the new design based-SVM of the CNN classifier architecture with dropout performs significantly more efficiently than CNN based-SVM model without dropout and the standard CNN classifier. The performance of our model is compared with character recognition accuracies gained from state-of-the-art Arabic Optical Character Recognition, producing favorable results.
Lately, Online Arabic Handwriting Recognition has been gaining more interest because of the advances in technology such as the handwriting capturing devices and impressive mobile computers. And since we always try to improve recognition rates, we propose in this work a new system based on a deep recurrent neural networks on which the dropout technique was applied. Our approach is very practical in sequence modelling due to their recurrent connections, also it can learn intricate relationship between input and output layers because of many non-linear hidden layers. In addition to these contributions, our system is protected against overfitting due to powerful performance of dropout. This proposed system was tested with a large dataset ADAB to show its performance against difficult conditions as the variety of writers, the large vocabulary and diversity of style.
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