Abstract This paper presents diacritics rule-based part-of-speech (POS) tagger which automatically tags a partially vocalized Arabic text. The aim is to remove ambiguity and to enable accurate fast automated tagging system. A tagset is being designed in support of this system. Tagset design is at an early stage of research related to automatic morphosyntactic annotation in Arabic language. Preliminary results of... This paper presents diacritics rule-based part-of-speech (POS) tagger which automatically tags a partially vocalized Arabic text. The aim is to remove ambiguity and to enable accurate fast automated tagging system. A tagset is being designed in support of this system. Tagset design is at an early stage of research related to automatic morphosyntactic annotation in Arabic language. Preliminary results of the tagset design have been reported in this paper. Arabic language has a valuable and important feature, called diacritics, which are marks placed over and below the letters of Arabic word. This feature plays a great role in adding linguistic attributes to Arabic words and in indicating pronunciation and grammatical function of the words. This feature enriches the language syntactically while removing a great deal of morphological and semantically ambiguities. Read More Read Less

Abstract Building a high performance classifier requires an efficient training algorithm as well as a high performance testing algorithm .With the present effort, we propose to focus on the development of an automated Maintainable information classifying system as a main goal. The system has two phases: learning phase and testing phase. On the one hand, the system accepts a set of... Building a high performance classifier requires an efficient training algorithm as well as a high performance testing algorithm .With the present effort, we propose to focus on the development of an automated Maintainable information classifying system as a main goal. The system has two phases: learning phase and testing phase. On the one hand, the system accepts a set of Arabic classified documents as a real training data set, during its learning phase. The system learning technique based on the so- called Maximum Entropy Model. The model enables the system to learn the parameters weights. On the other hand, the system accepts, during its testing phase, any randomly selected Arabic unclassified document or documents. Then it uses the estimated weights, learned so far, to decide whether that document or documents belongs or belong to one or more predefined categories, depend on their context. The maximum entropy model was implemented. Hence, making a conclusion that emphasizes the model efficiency for Arabic text categorization in terms of learning speed, real time classification speed, and classification accuracy. Read More Read Less

Abstract In this paper, we propose a robust approach for text extraction and recognition from Arabic news video sequence. The text included in video sequences is an important needful for indexing and searching system. However, this text is difficult to detect and recognize because of the variability of its size, their low resolution characters and the complexity of the backgrounds. To... In this paper, we propose a robust approach for text extraction and recognition from Arabic news video sequence. The text included in video sequences is an important needful for indexing and searching system. However, this text is difficult to detect and recognize because of the variability of its size, their low resolution characters and the complexity of the backgrounds. To solve these problems, we propose a system performing in two main tasks: extraction and recognition of text. Our system is tested on a varied database composed of different Arabic news programs and the obtained results are encouraging and show the merits of our approach. Read More Read Less

Abstract This work explores the use of a discrete wavelet transform, a feature extractor mechanism for speech recognition. Speech recognition is a fascinating application of digital signal processing offering unparalleled opportunities. The real-world applications deploying speech recognition and its implications can be varied across various fields. Speech recognition can automate many tasks that previously required hands-on human interaction. Accurate vowel recognition... This work explores the use of a discrete wavelet transform, a feature extractor mechanism for speech recognition. Speech recognition is a fascinating application of digital signal processing offering unparalleled opportunities. The real-world applications deploying speech recognition and its implications can be varied across various fields. Speech recognition can automate many tasks that previously required hands-on human interaction. Accurate vowel recognition forms the backbone of most successful speech recognition systems. The vowel set of Malayalam, one of the South Indian languages, is used to create the database. A hybrid approach with discrete wavelet transforms and neural networks are used to form a system with improved performance. Daubechies wavelet is employed in this experiment. Features are extracted by using Discrete Wavelet Transforms (DWT). Training, testing and pattern recognition are performed using Artificial Neural Networks (ANN). The results show excellent overall recognition accuracy above 95%. The high accuracy obtained shows promising potentials of discrete wavelet transforms and neural networks in speech recognition. Read More Read Less