Papers by MAROUAN ELMANSOURI
Handwritten Arabic, like other handwritten (such as Latin, Chinese, etc.), have received increasi... more Handwritten Arabic, like other handwritten (such as Latin, Chinese, etc.), have received increasing 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 For this reason, traditional methods of indexing such as annotation or transcription will be outdated as they require a considerable and unreliable manual effort.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 the 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
Toward Classification of Arabic Manuscripts Words Based on the Deep Convolutional Neural Networks
Deep learning is an area that has seen many developments in recent years. One of these algorithms... more Deep learning is an area that has seen many developments in recent years. One of these algorithms that have provided good results is Deep Convolutional Neural Networks (DCNN). It is proven to be effective in various fields such as natural language processing, pattern recognition, computer vision, object detection in images, etc. Despite the development of these technologies, Arabic manuscripts in digital libraries still use traditional indexing methods based on metadata, annotation or transcription. In this article, we propose two methods of word classification based on deep learning, the first one uses a simple Neural Network (DNN) and the last one uses a Convolutional Neural Network (DCNN). The idea is to segment words of Arabic manuscripts images and predict the class of each word. The experimental results show the efficient of this classification system based on the DCNN. By comparing the results obtained, we can observe that the DCNN method provides excellent results than those obtained with the DNN method.
Toward Classification of Arabic Manuscripts Words Based on the Deep Convolutional Neural Networks
2020 International Conference on Intelligent Systems and Computer Vision (ISCV)
Deep learning is an area that has seen many developments in recent years. One of these algorithms... more Deep learning is an area that has seen many developments in recent years. One of these algorithms that have provided good results is Deep Convolutional Neural Networks (DCNN). It is proven to be effective in various fields such as natural language processing, pattern recognition, computer vision, object detection in images, etc. Despite the development of these technologies, Arabic manuscripts in digital libraries still use traditional indexing methods based on metadata, annotation or transcription. In this article, we propose two methods of word classification based on deep learning, the first one uses a simple Neural Network (DNN) and the last one uses a Convolutional Neural Network (DCNN). The idea is to segment words of Arabic manuscripts images and predict the class of each word. The experimental results show the efficient of this classification system based on the DCNN. By comparing the results obtained, we can observe that the DCNN method provides excellent results than those obtained with the DNN method.
Handwritten Arabic, like other handwritten (such as Latin, Chinese, etc.), have received increasi... more Handwritten Arabic, like other handwritten (such as Latin, Chinese, etc.), have received increasing 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 the 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
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Papers by MAROUAN ELMANSOURI