Handwritten Arabic Classification Using Deep Convolutional Neural Networks

EasyChair Preprint 3247

Abstract

Handwritten Arabic, like other handwritten (such as Latin, Chinese, etc.), have received an increase attention from several researchers. To preserve and promote wider access to the invaluable cultural and literary heritage held in both public and private collections of manuscripts, the researchers have proposed and developed several approaches based on annotation, metadata, and transcription. The need to access to the manuscript text is increasing on a large scale. For this reason, traditional methods of indexing such as annotation or transcription will be outdated as they require a considerable and unreliable manual effort. It is therefore necessary to develop new tools for the identification and recognition of handwritten text contained in images. However, despite the development that has been shown by Convolutional Neural Network (CNN) in different computer vision tasks, the latter has not known many uses in the field of Arabic manuscripts. Even if, the use of these methods based on deep learning to predict the class of characters, such as the Handwritten numbers, has achieved a great result. Hence, the idea of using methods based on deep learning techniques to classify words and characters in images of Arabic manuscripts. In this paper, we propose two classification methods to predict the class of each word, using HADARA80P dataset. The first one uses a simple Neural Network and the last one uses a convolutional neural network. The experimental results obtained by these two methods are very interesting.

Keyphrases: Deep Convolutional Neural Network, HADARA80P Dataset, Handwritten Arabic, Neural Network., classification, computer vision, image processing

@booklet{EasyChair:3247,
  author    = {Marouan Elmansouri and Noureddine El Makhfi and Badraddine Aghoutane},
  title     = {Handwritten Arabic Classification Using Deep Convolutional Neural Networks},
  howpublished = {EasyChair Preprint 3247},
  year      = {EasyChair, 2020}}