Volker Märgner

Arabic Handwriting Recognition Competition

This paper describes the Arabic handwriting recognition competition held at ICDAR 2007. This second competition (the first was at ICDAR 2005) again uses the IFN/ENIT-database with Arabic handwritten Tunisian town names. Today, more than 54 research groups from universities, research centers, and industry are working with this database worldwide. This year, 8 groups with 14 systems are participating in the competition. The systems were tested on known data and on two datasets which are unknown to the participants. The systems are compared on the most important characteristic, the recognition rate. Additionally, the relative speed of the different systems were compared. A short description of the participating groups, their systems, and the results achieved are finally presented.

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.

Baseline estimation for Arabic handwritten words

Baseline information has been used for diverse purposes in handwriting research. The baseline represents a first orientation in a word and it is often a precondition for subsequent algorithms, including preprocessing tasks, segmentation and feature extraction for recognition systems. Approaches based on the horizontal projection histogram are used for Arabic printed text but they are ill-suited for Arabic handwritten words. In this paper we present a method that is completely based on polygonally approximated skeleton processing. The central algorithm is concerned with finding features in the skeleton and processing linear regression analysis. Our method performs very well as long as the model assumption of one straight line applies. We tested the method on 26459 isolated Tunisian town names written by 411 writers (IFNIENIT-database).

Detection of foreign bodies in food by thermal image processing

This paper deals with the problem of detection of foreign bodies in food. A new method for inspecting food samples is presented, using thermographic images to detect foreign bodies that are not detectable using conventional methods. At first, the basic background of thermography is given. Then, experiments to obtain well-contrasted thermographic images of different food and foreign bodies are discussed. The main part of the present paper introduces specific image processing methods that show a good recognition power of foreign bodies within food. Results achieved with a small set of test images are presented. The results are promising and the methods work even on some poorly contrasted images. To compare the different image processing and recognition methods, a quality index is defined. On the test images the success of the presented methods is shown and the difference in recognition results can be measured using the introduced quality index.

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.

Comparison of Different Preprocessing and Feature Extraction Methods for Offline Recognition of Handwritten ArabicWords

Preprocessing and feature extraction are very important steps in automatic cursive handwritten word recognition. Based on an offline recognition system for Arabic handwritten words which uses a semi-continuous 1-dimensional Hidden Markov Model recognizer, different preprocessing combined with different feature sets are presented. The dependencies of the feature sets from preprocessing steps are discussed and their performances are compared using the IFN/ENIT-database of handwritten Arabic words. As the lower and upper baseline of each word are part of the ground truth of the database, the dependency of the feature set from the accuracy of the estimated baseline is evaluated.

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.

KHATT: An open Arabic offline handwritten text database

Abstract A comprehensive Arabic handwritten text database is an essential resource for Arabic handwritten text recognition research. This is especially true due to the lack of such database for Arabic handwritten text. In this paper, we report our comprehensive Arabic offline Handwritten Text database (KHATT) consisting of 1000 handwritten forms written by 1000 distinct writers from different countries. The forms were scanned at 200, 300, and 600 dpi resolutions. The database contains 2000 randomly selected paragraphs from 46 sources, 2000 minimal text paragraph covering all the shapes of Arabic characters, and optionally written paragraphs on open subjects. The 2000 random text paragraphs consist of 9327 lines. The database forms were randomly divided into 70%, 15%, and 15% sets for training, testing, and verification, respectively. This enables researchers to use the database and compare their results. A formal verification procedure is implemented to align the handwritten text with its ground truth at the form, paragraph and line levels. The verified ground truth database contains meta-data describing the written text at the page, paragraph, and line levels in text and XML formats. Tools to extract paragraphs from pages and segment paragraphs into lines are developed. In addition we are presenting our experimental results on the database using two classifiers, viz. Hidden Markov Models (HMM) and our novel syntactic classifier. The database is made freely available to researchers world-wide for research in various handwritten-related problems such as text recognition, writer identification and verification, forms analysis, pre-processing, segmentation. Several international research groups/researchers acquired the database for use in their research so far.

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.