Aymen Chaabouni

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.

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.

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.

Off-Line Features Integration for On-Line Handwriting Graphemes Modeling Improvement

This paper deals with the improvement of an on-line Arabic handwriting modeling system based on graphemes segmentation. The presented strategy consists in the integration of off-line features to assimilate and take up the handwriting style variation in a multi-writer context. The main contribution of the presented work consists in making off-line fuzzy template for each on-line segmented graphemes trajectory and the extraction of geometric moments invariants by using a method adapted to the irregular spatial sampling of their on-line trajectory. The experimental results prove the added value of the introduced features on the discriminative power of the developed handwriting modeling system.

Off-lexicon online Arabic handwriting recognition using neural network

This paper highlights a new method for online Arabic handwriting recognition based on graphemes segmentation. The main contribution of our work is to explore the utility of Beta-elliptic model in segmentation and features extraction for online handwriting recognition. Indeed, our method consists in decomposing the input signal into continuous part called graphemes based on Beta-Elliptical model, and classify them according to their position in the pseudo-word. The segmented graphemes are then described by the combination of geometric features and trajectory shape modeling. The efficiency of the considered features has been evaluated using feed forward neural network classifier. Experimental results using the benchmarking ADAB Database show the performance of the proposed method.

Hybrid Neural Network and Genetic Algorithm for off-Lexicon Online Arabic Handwriting Recognition.

In this paper we propose the hybridization of neural networks and genetic algorithm for online Arabic handwriting recognition. The used method consists in decomposing the input signal into continuous parts called graphemes based on Beta-Elliptical model and baseline detection. The segmented graphemes are then described according to their position in the pseudo-word by a combination of geometric features modeling their trajectory shape and provided in the input of the neural networks used for graphemes class recognition. Finally, a genetic algorithm is used to generate the characters code corresponding to the obtained chain of recognized graphemes code by applying the genetic search process: selection, crossover and mutation. The developed system is evaluated using an Arabic words dataset extracted from the ADAB Database.