Monji Kherallah

On-line handwritten digit recognition based on trajectory and velocity modeling

The handwriting is one of the most familiar communication media. Pen based interface combined with automatic handwriting recognition offers a very easy and natural input method. The handwritten signal is on-line collected via a digitizing device, and it is classified as one pre-specified set of characters. The main techniques applied in our work include two fields of research. The first one consists of the modeling system of handwriting. In this area, we developed a novel method of the handwritten trajectory modeling based on elliptic and Beta representation. The second part of our work shows the implementation of a classifier consisting of the Multi-Layers Perception of Neural Networks (MLPNN) developed in a fuzzy concept. The training process of the recognition system is based on an association of the Self Organization Maps (SOM) with Fuzzy K-Nearest Neighbor Algorithms (FKNNA). To test the performance of our system we build 30,000 Arabic digits. The global recognition rate obtained by our recognition system is about 95.08%.

Combining Multiple HMMs Using On-line and Off-line Features for Off-line Arabic Handwriting Recognition

This paper presents an off-line Arabic Handwriting recognition system based on the selection of different state of the art features and the combination of multiple Hidden Markov Models classifiers. Beside the classical use of the off-line features, we add the use of on-line features and the combination of the developed systems. The designed recognizer is implemented using the HMM-Toolkit. In a first step, we use different features to make the classification and we compare the performance of single classifiers. In a second step, we proceed to the combination of the on-line and the off-line based systems using different combination methods. The system is evaluated using the IFN/ENIT database. The recognition rate is in maximum 63.90% for the individual systems. The combination of the on-line and the off-line systems allows to improve the system accuracy to 81.93% which exceeds the best result of the ICDAR 2005 competition.

Online Arabic handwriting recognition: a survey

Researches on handwriting recognition have known a great attention since it has been considered as a technological revolution in man-machines interfaces especially that handwriting has continued to persist as the most used mean of communication and recording information in day-to-day life. The challenging nature of handwriting recognition and segmentation has attracted the attention of researchers from academic and industry circles. The huge part of these researches deals with Latin and Chinese. Interest in Arabic script comes years later, and so the state of the art is less advanced. This survey describes the nature of this Arabic handwritten language and the basic concepts behind the recognition process. An overview of the state of the art of online Arabic handwriting recognition is presented. It is based on an extensive review of the literature in order to describe background in the field, discussion of the methods, and future research directions. It is the first survey to focus on online Arabic handwriting recognition and provide recognition rates and descriptions of database used for the discussed approaches.

Online Arabic Handwriting Recognition Competition

Arabic script presents a challenge complexity and variability for handwriting recognition. The first on line Arabic Database called ADAB is known as a standard benchmark in the ICDAR competition of 2009. This paper describes the Online Arabic handwriting recognition competition held at ICDAR 2011. 3 groups with 5 systems are participating in the competition. The systems were tested on known data (sets 1 to 4) and on two test datasets which are unknown to all participants (set 5 and set 6). The systems are compared on the most important characteristic of classification systems, the recognition rate. Additionally, the relative speed of every system was compared. A short description of the participating groups, their systems, the experimental setup, and the performed results are presented.

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

On-line Arabic handwriting recognition competition: ADAB database and participating systems

This paper describes the on-line Arabic handwriting recognition competition held at tenth International Conference on Document Analysis and Recognition (ICDAR in Proceedings of the 10th international conference on document analysis and recognition, vol 3, pp 1388–1392, 2009). This first competition uses the so-called ADAB database with Arabic on-line handwritten words. At this first competition, 3 groups with 7 different systems have participated. The systems were tested on known data (training datasets made available for the participants, sets 1 to 3) and on one test dataset that is unknown to all participants (set 4). The systems are compared on the most important characteristic of classification systems, the recognition rate. Additionally, the relative speed of the different systems was compared. A short description of the participating groups, their systems, the experimental setup, and the performed results is presented.

A New Design Based-SVM of the CNN Classifier Architecture with Dropout for Offline Arabic Handwritten Recognition

In this paper we explore a new model focused on integrating two classifiers; Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for offline Arabic handwriting recognition (OAHR) on which the dropout technique was applied. The suggested system altered the trainable classifier of the CNN by the SVM classifier. A convolutional network is beneficial for extracting features information and SVM functions as a recognizer. It was found that this model both automatically extracts features from the raw images and performs classification. Additionally, we protected our model against over-fitting due to the powerful performance of dropout. In this work, the recognition on the handwritten Arabic characters was evaluated; the training and test sets were taken from the HACDB and IFN/ENIT databases. Simulation results proved that the new design based-SVM of the CNN classifier architecture with dropout performs significantly more efficiently than CNN based-SVM model without dropout and the standard CNN classifier. The performance of our model is compared with character recognition accuracies gained from state-of-the-art Arabic Optical Character Recognition, producing favorable results.

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.