Intelligent handwritten recognition using hybrid CNN architectures based-SVM classifier with dropout

Text recognition in Arabic handwritten scripts is an active research field. These recognition systems face numerous challenges, including enormous open data-bases, infinite variation in people's handwriting, and freestyle. In this manuscript, Authors model deep learning architecture which can efficiently be utilized to recognizing Arabic handwritten scripts. This work explored a new model for both single font and multi-font type which concentrate on two common classifiers which are: Support Vector Machine (SVM) along with Convolutional Neural Network (CNN). Furthermore, authors protected the proposed model against the issue of over-fitting because of the strong performance of dropout technique. Both classification and feature extraction are done automatically. In the light of the error backpropagation method analysis, authors also have been proposed an innovative depth neural network training rule for maximum interval minimum classification error. In the meantime, max-margin minimum classification error (M3CE) and cross entropy are analyzed and hybridized to obtain better outcomes. Authors tested the proposed model on AHDB, AHCD, HACDB, and IFN/ENIT databases. The proposed model performance is compared with the accuracies of text recognition gained from state-of-the-art Arabic text recognition. The proposed model delivers favorable results. (C) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University.

Automated summary

Text recognition in Arabic handwritten scripts is a challenging research field. In this study, authors propose a deep learning model for efficiently recognizing Arabic handwritten scripts. The model combines Support Vector Machine (SVM) and Convolutional Neural Network (CNN) classifiers for classification and feature extraction, and addresses the issue of over-fitting using dropout technique. The proposed model achieves favorable results in text recognition accuracy, as demonstrated through testing on multiple databases.