Haikal El Abed

ICDAR 2005 Arabic handwriting recognition competition

This paper describes the Arabic handwriting recognition competition held at International Conference on Document Analysis and Recognition (ICDAR) 2011. This fifth competition again used the IfN/ENIT-database with Arabic handwritten Tunisian town names. Today, more than 110 research groups from universities, research centers, and industry are working with this database worldwide. This year, 4 groups with 4 systems were participating in the competition. The systems were tested on known data (sets d and e) and on two data sets which are unknown to the participants (sets f and s). The systems were compared based on the most important characteristic: the recognition rate. A short description of the participating groups, their systems, and the results achieved are finally presented.

ICDAR 2009 Handwriting Recognition Competition

This paper describes the handwriting recognition competitionheld at ICDAR 2009. This competition is based onthe RIMES-database, with French written text documents.These document are classified in three different categories,complete text pages, words, and isolated characters. Thisyear 10 systems were submitted for the handwritten recognitioncompetition on snippets of French words. The systemswere evaluated in three subtask depending of the sizes ofthe used dictionary. A comparison between different classificationand recognition systems show interesting results. Ashort description of the participating groups, their systems,and the results achieved are presented.

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 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.

A comparative study of the improvement of performance using a PSO modified by ACO applied to TSP

Swarm-inspired optimization has become very popular in recent years. Particle swarm optimization (PSO) and Ant colony optimization (ACO) algorithms have attracted the interest of researchers due to their simplicity, effectiveness and efficiency in solving complex optimization problems. Both ACO and PSO were successfully applied for solving the traveling salesman problem (TSP). Performance of the conventional PSO algorithm for small problems with moderate dimensions and search space is very satisfactory. As the search, space gets more complex, conventional approaches tend to offer poor solutions. This paper presents a novel approach by introducing a PSO, which is modified by the ACO algorithm to improve the performance. The new hybrid method (PSO-ACO) is validated using the TSP benchmarks and the empirical results considering the completion time and the best length, illustrate that the proposed method is efficient.

Databases and competitions: strategies to improve Arabic recognition systems

The great success and high recognition rates of both OCR systems and recognition systems for handwritten words are unconceivable without the availability of huge datasets of real world data. This chapter gives a short survey of datasets used for recognition with special focus on their application. The main part of this chapter deals with Arabic handwriting, datasets for recognition systems, and their availability. A description of different datasets and their usability is given, and the results of a competition are presented. Finally, a strategy for the development of Arabic handwriting recognition systems based on datasets and competitions is presented.

ICDAR 2009 Online Arabic Handwriting Recognition Competition

This paper describes the Online Arabic handwriting recognition competition held at ICDAR 2009. This first competition uses the ADAB-database with Arabic online handwritten words. This year, 3 groups with 7 systems are participating in the competition. The systems were tested on known data (sets 1 to 3) and on one test dataset which 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 were compared. A short description of the participating groups, their systems, the experimental setup, and the performed results are presented.

ICFHR 2010 - Arabic Handwriting Recognition Competition

This paper describes the Arabic handwriting recognition competition held at International Conference on Frontiers in Handwriting Recognition (ICFHR 2010) in Kolkata, India. This fourth competition (the first was at ICDAR 2005 in Seoul, South Korea, the second at ICDAR 2007 in Curitiba, Brazil and the third at ICDAR 2009 in Barcelona, Spain) again used the IfN/ENIT-database with Arabic handwritten Tunisian town names. Today, more than 100 research groups from universities, research centers, and industry are working with this database worldwide. This year, 4 groups with 6 systems participated at the competition. The systems were tested on known data and on two data sets which were unknown to the participants. The systems were compared based on the most important characteristic: the recognition rate. Additionally, the relative speed of the different systems was compared. A short description of the participating groups, their systems, and the results achieved are finally presented.

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.

ICDAR 2009-Arabic handwriting recognition competition

This paper describes the Arabic handwriting recognition competition held at ICDAR 2009. This third competition (the first two were held at ICDAR 2005 and 2007, respectively) again used the IfN/ENIT-database with Arabic handwritten Tunisian town names. This very successful database is used today by more than 82 research groups from universities, research centers, and industries worldwide. At ICDAR 2009, 7 groups with 17 systems participated in the competition. The system evaluation was made on one known dataset and on two datasets unknown to the participants. The systems were compared based on the recognition rates achieved. Additionally, the relative speeds of the systems were compared. A description of the participating groups, their systems, and the results achieved are presented. As a very important result of this competition, a continuous improvement of the recognition rate from competition to competition of more than 5% can be observed.