Predict if a handwritten document has been produced by a male or a female writer
The prediction of gender from handwriting is a very interesting research field.
It has many applications including the forensic application where it can help investigators focusing more on a certain category of suspects. But, is it accurate?
There are a few studies regarding the automatic detection of the gender of a handwritten document [1-3].
The aim of this competition is to attract the interest of the document analysis community to this research area and to measure the performance of recent advances in this field.
The dataset used in this study has been described in this paper [4].
A total of 475 writers produced 4 handwritten documents:
the first page contains an Arabic handwritten text which varies from one writer to another.
the second page contains an Arabic handwritten text which is the same for all the writers.
the third page contains an English handwritten text which varies from one writer to another.
and the fourth page contains an English handwritten text which is the same for all the writers.
The training set consists of the first 282 writers for which the genders are provided.
Participants are asked to predict the gender of the remaining 193 writers.
For participants who are not familiar with digital image-processing, a set of features extracted from all the images will be provided. Those features are similiar to that of the previous 2011 and 2012 Arabic Writer Identification Contests. Those features are described in [5].
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This competition is organized in the scope of the Twelfth International Conference on Document Analysis and Recognition ICDAR2013 that will be held in Washington, DC.
[1] Bandi, K., Srihari, S.N., Writer demographic identification using bagging and boosting. In: Proc. International Graphonomics Society Con- ference (IGS). pp. 133–137 (2005).
[2] Liwicki, M., Schlapbach, A., Loretan, P., Bunke, H., Automatic detection of gender and handedness from online handwriting. In: Proc. 13th Conference of the International Graphonomics Society. pp. 179–183 (2007).
[3] Liwicki, M., Schlapbach, A., Bunke, H., Automatic gender detection using on-line and off-line information. Pattern Analysis and Applications 14, 87–92 (2011).
[4] Al-Ma’adeed, S., Ayouby, W., Hassaine, A., Aljaam, J., QUWI: An Arabic and English Handwriting Dataset for Offline Writer Identification. In: Frontiers in Handwriting Recognition, International Conference on. Bari, Italy (September 2012).
[5] Hassaïne, A., Al-Maadeed, S. and Bouridane, A., A Set of Geometrical Features for Writer Identification. Neural Information Processing. Springer Berlin/Heidelberg, 2012
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