To cite this version:Yousri Kessentini, Thierry Paquet, Abdelmajid Benhamadou. Off-line handwritten word recognition using multi-stream hidden Markov models. Pattern Recognition Letters, Elsevier, 2010, 31 (1) AbstractIn this paper, we present a multi-stream approach for off-line handwritten word recognition. Using 2-stream approach, the best recognition performance is 79.8%, in the case of the Arabic script, on a 2100-word lexicon consisting of 946 Tunisian town/village names. In the case of the Latin script, the proposed approach achieves a recognition rate of 89.8 % using a lexicon of 196 words.2 KeywordsOff-Line handwriting recognition, Hidden Markov Models, Latin script, Arabic script, multistream, information combination.

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In this paper, we address the problem of the large coverage dictionaries of Arabic language usable both for direct human reading and automatic Natural Language Processing. For these purposes, we propose a normalized and implemented modeling, based on Lexical Markup Framework (LMF-ISO 24613) and Data Registry Category (DCR-ISO 12620), which allows a stable and well-defined interoperability of lexical resources through a unification of the linguistic concepts. Starting from the features of the Arabic language, and due to the fact that a large range of details and refinements need to be described specifically for Arabic, we follow a finely structuring strategy. Besides its richness in morphology, syntax and semantics knowledge, our model includes all the Arabic morphological patterns to generate the inflected forms from a given lemma and highlights the syntactic-semantic relations. In addition, an appropriate codification has been designed for the management of all types of relationships among lexical entries and their related knowledge. According to this model, a dictionary named El Madar 1 has been built and is now publicly available on line. The data are managed by a user-friendly Web-based lexicographical workstation. This work has not been done in isolation, but is the result of a collaborative effort by an international team mainly within the ISO network during a period of eight years. A. Khemakhem et al.is to merge them in order to obtain a new richer resource. More generally, the exchange remains a difficult (and expensive) issue when nothing has been scheduled for this purpose. To meet this challenge, several projects were conducted such as ACQUILEX (Bogurev et al. These projects led to the emergence of the LMF (Lexical Markup Framework) ISO standard for the lexical structure modeling (ISO 24613) (Francopoulo 2003), (Francopoulo and George 2008) in association with the ISO Data Categories Registry (DCR) 3 following ISO 12620 (Ide and Romary 2004). These standards were designed by a group of sixty ISO experts coming from different cultures, languages and continents. Numerous developments followed in different parts of the world. 4 Unfortunately, the Arabic language did not immediately benefit from the emergence of these standards, although it is spoken by more than 300 million people around the world, and is the official language of more than twenty countries. The language still uses references to different printed dictionaries based on incompatible lexicographical schools. Only few works tried the application of LMF on the Arabic language out, according to previous revisions of this standard. Some developments were made in morphology (Khemakhem, Gargouri and Abdelwahed 2006), (Romary, Salmon-Alt and Francopoulo 2004), (Salmon-Alt, Akrout and Romary 2005) and some studies were conducted in syntax (Loukil, Haddar and Ben Hamadou 2008). However, these works were developed during the drafting of the LMF standard and were not updated according to the ISO validation.Obviously, the situation of the Arabic lexica...

The measure of sentence similarity is useful in various research fields, such as artificial intelligence, knowledge management, and information retrieval. Several methods have been proposed to measure the sentence similarity based on syntactic and/or semantic knowledge. Most proposals are evaluated on English sentences where the accuracy can decrease when these proposals are applied to other languages. Moreover, the results of these methods are unsatisfactory, as much relevant semantic knowledge, such as semantic class, thematic role and syntactico-semantic knowledge like the semantic predicates, are not taken into account. We must acknowledge that this kind of knowledge is rare in most of the lexical resources. Recently, the International Organization for Standardization (ISO) has published the Lexical Markup Framework (LMF) ISO-24613 norm for the development of lexical resources. This norm provides, for each meaning of a lexical entry, all the semantic and syntactico-semantic knowledge in a fine structure. Profiting from the availability of LMF-standardized dictionaries, we propose, in this paper, a generic method that enhances the measure of sentence similarity by applying semantic and syntactico-semantic knowledge. An experiment was carried out on Arabic, as this language is processed within our research team and an LMF-standardized Arabic dictionary is at hand where the semantic and the syntactico-semantic B Wafa Wali

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