Abstract-The automatic recognition of handwriting is a particularly complex operation. Until now, there is no algorithm able to recognize handwriting without that; there are assumptions to take in advance to facilitate the task of the process. A handwritten text can contain letters lowercase, uppercase letters, characters sticks and digits. Therefore, it is capital to know extract and separate all these different units in order to process them with specific algorithms to their class handwriting.In this paper, we present a system for unconstrained handwritten text recognition, which allows to achieve this operation thanks to an intelligent segmentation based on an iterative cutting in a multi-script environment.The results obtained from the experimental protocol reach an "equal error rate" (EER) neighboring to 6%. These calculations were calculated with a relatively small base; however this same rate can be decreased with great bases. Our results are extremely encouraging for the simple reason that our approach is situated in a more general context unlike other approaches which define several non-rigid assumptions; this clearly makes the problem simpler and may make it trivial.
This paper presents a novel approach to extract information for building ontologies for an extensive range of applications from corpora. Our goal is to propose a method that is independent of domains and based on a distributional analysis of semantic units to bring out all the candidate’s informative elements (concepts, entities, semantic relations, named entities etc.). This method is based on a pipeline of four main stages allows for the extraction of information from unstructured text in the form of a suite of decomposable representations (sentences in triplets, ‘argumental structure’ etc.) until a consistent final ontology is obtained. We applied the defined pipeline a repeated sampling of 100 articles randomly drawn from a text corpus (‘Le Monde’ of annual version ‘2013’). The evaluation results of the trial implementation of our system level of accuracy to be up to 74%. The results obtained indicate that the proposed methodology is quite generic and can be easily adapted to any new domain.
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