Online Arabic Handwriting Recognition Competition Monji Kherallah, Najiba Tagougui, Adel M. Alimi University of Sfax, Research Group on Intelligent Machines (REGIM) Sfax,Tunisia {monji.kherallah,najiba.tagougui,adel.alimi}@ieee.org Haikal El Abed, Volker M¨argner Technische Universitaet Braunschweig, Institute for Communications Technology (IfN), Braunschweig, Germany {v.maergner ,elabed}@tu-bs.de Abstract—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. Keywords-On line Handwriting; ADAB-database; systems; recognition rate. I. INTRODUCTION In few last years, the handwriting analysis and recognition is a paramount subject of the researchers interest. The validation of the works done in this area was successfully established thank to the databases use. Two sorts of databases are considered. One interests the on line studies like UNIPEN and the other interests the off line studies like (CEDAR, IRONOFF, NIST, IFN/ENIT, etc.). All these databases are important for the research community in order to test new ideas and algorithms and to perform benchmarks and thereby measure progress and general tendencies. Large databases were developed for the handwriting recognition in Latin scripts. In contrast, very few databases have been developed for the Arabic script and fewer have become publicly available. On line recognition of the cursive Arabic handwritten words, aims to contribute in the evolution of on line Arabic handwriting recognition research. Since 2009 the freely available (ADAB data base) is used by some groups all over the world to develop on line Arabic handwriting recognition systems. This database was the basis for the competition of ICDAR’2009 for systems that are specialized in on line recognition of the cursive Arabic handwritten words. This ICDAR’2011 competition uses as a next step the same background of the ADAB database but now with and extended collected data of freely written words. These sets are unknown to all participants. Note that the writers of the set 5 are adults, whereas set 6 it consists of a collection made by young school students (between 9 and 13 years old). A comparison and discussion of different algorithms and recognition methods should give a push in the field of on line Arabic handwritten word recognition. Our paper is written as follow: The next section describes the evaluation process. Section 3 gives the details of ADAB-database. Section 4 presents the participants and their systems description. Section 5 deals with results and discussion. The last section announces the winner of this competition and the future prospects. II. EVALUATION PROCESS The object is to run each Arabic handwritten word recognizer (trained on a part of version 2.0 of the ADAB- database) on an already published part of the ADAB- database and on a test set not included in the published part. The recognition results on word level of each system are compared on the basis of correct recognized words, i.e. there correspondent consecutive Numeric Character References (NCR). A dictionary can be used in the recognition process. A recognizer may return up to 10 candidates for each classification that not only the first ranked result can be used for comparison but also the correct result between the 5 or 10 candidates will be used for comparison. The evaluation process of all systems will be released in our laboratory REGIM: Group of Research on Intelligent Machines. We run the recognizer (called myrec) by invoking it from the command line as follows: myrec input.txt output.txt. Fig. 1 presents an example of the input file which is just a list of relative paths to each *.inkml online trace to be recognized. The output file should have one line as result for each input file. Each line should show the name of the online 2011 International Conference on Document Analysis and Recognition 1520-5363/11 $26.00 © 2011 IEEE DOI 10.1109/ICDAR.2011.289 1454