Houcine Boubaker

Arabic Handwriting Recognition Using Restored Stroke Chronology

In this paper we present a system of the off-line handwriting recognition. Our recognition system is based on temporal order restoration of the off-line trajectory. For this task we use a genetic algorithm (GA) to optimize the sequences of handwritten strokes. To benefit from dynamic informations we make a sampling operation by the consideration of trajectory curvatures. We proceed to calculate the curvilinear velocity signal and use the beta-elliptical modelling which is developed in on-line systems to calculate other characteristics. Our approach is validated by Hmm Tool Kit (HTK) recognition system using IFN/ENIT database.

New Algorithm of Straight or Curved Baseline Detection for Short Arabic Handwritten Writing

In this paper we present a new method of baseline detection of online or offline short handwriting. This work is part of a large project for the edification of a dual online / offline Arabic handwriting recognition system. Compared to the existing approaches in the literature, this new method brings three specific novelties: First, the consideration of the agreement between the alignment of the points and their trajectory tangent directions for the detection of aligned points regroupings. Then, the consideration of a topologic characteristics specific to the used writing language, to value the pertinence of the pretender points regroupings to be recognized as baseline. Finally, we showed the aptitude of the algorithm to detect curved baseline

Fractal and Multi-fractal for Arabic Offline Writer Identification

In recent years, fractal and multi-fractal analysis have been widely applied in many domains, especially in the field of image processing. In this direction we present in this paper a novel method for Arabic text-dependent writer identification based on fractal and multi-fractal features; thus, from the images of Arabic words, we calculate their fractal dimensions by using the “Box-counting” method, then we calculate their multi-fractal dimensions by using the method of DLA (Diffusion Limited Aggregates). To evaluate our method, we used 50 writers of the ADAB database, each writer wrote 288 words (24 Tunisian cities repeated 12 times) with 2/3 of words are used for the learning phase and the rest is used for the identification. The results obtained by using knearest neighbor classifier, demonstrate the effectiveness of our proposed method.

Online Arabic Handwriting Modeling System Based on the Graphemes Segmentation

We present in this paper a new approach of online Arabic handwriting modeling based on the graphemes segmentation. This segmentation rests on the previous detection of baseline. It involves the detection of two types of topologically meaningful points: the backs of the valleys adjoining the baseline and the angular points. The stage of features extraction allows to model the shapes of segmented graphemes by relevant geometric parameters and to estimate their diacritics fuzzy affectation rates. The test results show a significant improvement in recognition rate with the introduction of new pertinent parameters.

Combining of Off-line and On-line Feature Extraction Approaches for Writer Identification

Writer identification still remains as a challenge area in the field of off-line handwriting recognition because only an image of the handwriting is available. Consequently, some information on the dynamic of writing, which is valuable for identification of writer, is unavailable in the off-line approaches, contrary to the on-line approaches where temporal and spatial information for the handwriting is available. In this paper we present a new method for writer identification based on Multi-Fractal features for both types of presented approaches. This method consists to extract the multi-fractal dimensions from the images of Arabic words and the on-line signals for the same words. In order to enhance the performance of our writer identification system, we have combined both on-line and off-line approaches, taking the advantage it provides ADAB database, which allows to recover the on-line signal and image for the same handwriting. In this way, our work consists to take advantage of static and dynamic representations of handwriting, in order to identify the writer in realistic conditions. The tests are performed on the writing of 100 writers from the ADAB database. The obtained results show the effectiveness of the proposed writer identification system.

Multi-fractal Modeling for On-line Text-Independent Writer Identification

The aim of this paper is to address the task of writer Identification of on-line handwriting. A new method for analytical on-line writer identification is proposed. However, although it is possible to measure the degree of handwriting irregularity thanks to the fractal dimension, the fractal analysis with a single exponent is not enough sufficient to characterize handwriting styles variation, instead, a continuous spectrum of exponents is necessary. In this purpose Multi-Fractal analysis was used to characterize styles of writing of writers. The main objective of this study is to explore the utility of this novel statistical tool for the purpose of distinguishing styles of on-line writings. Furthermore, a new method to estimate Multi-Fractal dimensions for on-line handwriting is presented and a procedure to find the most distinctive graphemes is elaborated. To evaluate our method, we have used the writings of 100 writers from the ADAB database. Our experimental results demonstrate the effectiveness of our proposed method and show a large capability of Multi-fractal features to characterize on-line handwriting styles.

Online Arabic Databases and Applications

Large databases were developed for handwriting recognition in Latin script. In contrast, very few databases have been developed for Arabic script, and fewer have become publicly available. This paper describes a pilot study in which we present the nature of the Arabic handwritten language and the basic concepts behind the recognition process. An overview of online Arabic databases and applications presented in the literature is discussed in detail. We also present some related works using these databases.

Fuzzy Segmentation and Graphemes Modeling for Online Arabic Handwriting Recognition

In this paper we present a new modeling approach for online Arabic handwriting which is based on fuzzy graphemes segmentation. In the literature, the result of the graphemes segmentation of a cursive writing not often reaches its optimum. This fact is due to the crisp aspect of the segmentation decision. In order to overcome this problem, we propose to introduce a fuzzy effect in this segmentation decision by overlapping the segmented graphemes in proportion to the confidence degrees associated with the detection of the particular points that separate them. The fuzzified boundary shapes of the extracted fuzzy graphemes are then modeled taking into account the coefficient of fuzzy membership of their points. The obtained results by using the ADAB database show an improvement of the recognition rate given by the fuzzy segmentation approach compared to the crisp one.

Online Arabic writer identification based on Beta-elliptic model

This paper proposes an automatic text-independent online Arabic writer identification system. The main contribution of our system is to explore the utility of Beta-elliptic model in features extraction for online writer identification, due to the rich output of Beta-elliptic model in terms of graphical, kinematical and biometrical data. The efficiency of the considered features has been evaluated using feed forward neural network classifier. Experimental results on ADAB Database show the performance of the proposed system in online Arabic writer identification task.

Spatiotemporal representation of 3D hand trajectory based on beta-elliptic models

The aim of this paper was to model the hand trajectory during grasping by an extension in 3D of the 2D written language beta-elliptic model. The interest of this model is that it takes into account both geometric and velocity information. The method relies on the decomposition of the task space trajectories in elementary bricks. The latter is characterized by a velocity profile modelled with beta functions and a geometry modelled with elliptic shapes. A data base of grasping movements has been constructed and the errors of reconstruction were assessed (distance and curvature) considering two variations of the beta-elliptic model (‘quarter ellipse’ and ‘two tangents points’ method). The results showed that the method based on two tangent points outperforms the quarter ellipse method with average and maximum relative errors of 2.73% and 8.62%, respectively, and a maximum curvature error of 9.26% for the former. This modelling approach can find interesting application to characterize the improvement due to a rehabilitation or teaching process by a quantitative measurement of hand trajectory parameters.