Sofiene Haboubi

Discrimination between Arabic and Latin from bilingual documents

An important task in machine learning is the electronic reading of documents. In this process, discrimination between languages is one of the first steps in the problem of automatic document text recognition. We are interested in the processing of mixed Arabic/Latin printed documents. Our method is based essentially on the extraction of words. We first extract structural features of words and then recognize the writing language. We finally present the results of our classification approach and discuss possible improvements.

Word classification in bilingual printed documents

In this paper we propose a method of identifying Arabic words from Arabic and Latin scripts in printed documents. This method is based on a statistical and geometric analysis to separate between words of a printed document. Structural features are used to describe the words extracted in previous step. Among the features used: the jambs, the diacritical points, the connected components, the hamps... From these characteristics, we construct our vector that allows the description. Functions of neural networks are used to classify the different words extracted. Classification is according to two classes Arabic or Latin. We present the found results of classification step, with a discussion on possible improvements.

Integrate reasoning using OWL ontology and SWRL rules to online character recognition approach

The problem of recognition of handwritten characters is still an active area of research which accord more attention. In this context, our paper focuses on purpose of a new approach based on the integration of reasoning logic using OWL ontology and SWRL rules to the classification phase. Our ontology consists of a set of concepts and spatial relations between them, the main concept is the Arabic character which is segmented into fine grained concept named stroke.

Separation between Arabic and Latin Scripts from Bilingual Text Using Structural Features

The identification of scripts is an important step in the characters recognition. In this work, we are interested in Arabic and Latin scripts. The identification is based on an approach to character recognition. The approach is structural feature. Selected characteristics of scripts are based on the geometric shape of the characters. Evaluations are conducted on a printed document Arabic and Latin, and with the neural networks functions. The results are interesting in the case of bilingual documents.

A segmentation-free approach to strokes extraction from online isolated Arabic handwritten character

With the massive changes in input acquisition systems such as smart phones and tablets, the field of handwriting recognition has more attention accorded by a several researchers. This article addresses the problem of online Arabic character segmentation. Our approach is based on top-down segmentation-free of Arabic character by detecting the candidate points in the general chain code of the character to extraction strokes. The main goal of our work to ameliorate the recognition rate.