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Proceedings Paper

Stroke frequency descriptors for handwriting-based writer identification
Author(s): Bart Dolega; Gady Agam; Shlomo Argamon
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Paper Abstract

Writer identification in offline handwritten documents is a difficult task with multiple applications such as authentication, identification, and clustering in document collections. For example, in the context of content-based document image retrieval, given a document with handwritten annotations it is possible to determine whether the comments were added by a specific individual and find other documents annotated by the same person. In contrast to online writer identification in which temporal stroke information is available, such information is not readily available in offline writer identification. The base approach and the main contribution of our work is the idea of using derived canonical stroke frequency descriptors from handwritten text to identify writers. We show that a relatively small set of canonical strokes can be successfully employed for generating discriminative frequency descriptors. Moreover, we show that by using frequency descriptors alone it is possible to perform writer identification with success rate which is comparable to the known state of the art in offline writer identification with close to 90% accuracy. As frequency descriptors are independent of existing descriptors, the performance of offline writer identification may be improved by combining both standard and frequency descriptors. Experimental evaluation with quantitative performance evaluation is provided using the IAM dataset.1

Paper Details

Date Published: 28 January 2008
PDF: 8 pages
Proc. SPIE 6815, Document Recognition and Retrieval XV, 68150I (28 January 2008); doi: 10.1117/12.767227
Show Author Affiliations
Bart Dolega, Illinois Institute of Technology (United States)
Gady Agam, Illinois Institute of Technology (United States)
Shlomo Argamon, Illinois Institute of Technology (United States)

Published in SPIE Proceedings Vol. 6815:
Document Recognition and Retrieval XV
Berrin A. Yanikoglu; Kathrin Berkner, Editor(s)

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