The ICDAR 2011 Arabic Handwriting Recognition Competition aims to bring together researchers working on Arabic handwriting recognition. Since 2002 the freely available IfN/ENIT-Database is used by more than 60 groups all over the world to develop Arabic handwriting recognition systems. This competition is the third in a series of competitions to establish the state of the art of recognizing Arabic handwritten words. Following the competition at ICDAR 2005, ICDAR 2007, ICDAR 2009, and ICFHR 2010 the fith competition takes place at the 11th International Conference on Document Analysis and Recognition (ICDAR2011), September 18-21, 2011, Beijing, China.

 

 

Evaluation Process:

The objective is to run each Arabic handwritten word recognizer (trained on the IfN/ENIT-Database, version 2.0) on an already published part of the IfN/ENIT-Database and on a new sample not yet published. The recognition results on word level of each system are compared on the basis of correct recognised words / respective there dedicated ZIP(Post)-Code. A dictionary can be used and should include all 937 different Tunisian town/village names.

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.

In a first comparison we use no reject. In the case that a reject is implemented in the recognizer, the recognition rate in respect to the reject rate will be compared in a second run.

Additionally the system can be tested on a new dataset, which includes not only words from the lexicon.

 

Running a Recognizer:

We run your recognizer (called myrec) by invoking it from the command line as follows: "myrec dataset.txt output.txt"?

• dataset.txt: The dataset is just a list of relative paths to each binary *.tif or *.bmp image to be recognized.
• output.txt: The output file should have one line for each input image. Each line should show the name of the image file that was recognized, followed by the responses (ZIP-Code of the Tunisian town/village name) for that image. Each response is given as a pair of values: the text, followed by the confidence. The following example shows that for image word/1/1.tif the recognizer has produced three word hypotheses: ZIP-Code 1000, 2000 and 3000, with confidences of 1.0, 0.8 and 0.4 respectively.

word/1/1.tif 1000 1.0 2000 0.8 3000 0.4

 

Important Dates:

Deadline for submission of systems: May 15, 2011

Haikal El Abed

Volker Märgner

Institute for Communications Technology (IfN)
Braunschweig Technical University
38106 Braunschweig, Germany
www.ifn.ing.tu-bs.de

 

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Updated: Monday, 18 April 2011