Optimization Of Dbn Using Regularization Methods Applied For Recognizing Arabic Handwritten Script

Since the mid 2010's, Deep learning has been regarded as a boom and consequently it has big success in a large field of applications like speech and pattern recognition. Handwriting recognition is indeed amongst the triumphal applications in the field of pattern recognition. Despite being quite matured, this field is still ambiguous for the Arabic handwritten script and hence several questions are still a challenge. In this study, Deep Belief Network (DBN) for Arabic handwritten script recognition is investigated. We then ensure DBN architecture against over-fitting because of mighty performance of dropout and dropconnect. Training with the both regularization methods, a randomly selected subsets of activations/weights are dropped. As a result, the evaluation on the HACDB database to deal with character level shows an improvement of classification error rate when using DBN trained with dropout or dropconnect. (C) 2017 The Authors. Published by Elsevier B.V.