Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Combining low-level features of offline questionnaires for handwriting identification

: Siegmund, Dirk; Ebert, Tina; Damer, Naser


Campilho, A.; Karray, F.:
Image analysis and recognition. Proceedings : 13th international conference, ICIAR 2016, in memory of Mohamed Kamel, Póvoa de Varzim, Portugal, July 13-15, 2016
Switzerland: Springer, 2016 (Lecture Notes in Computer Science (LNCS) 9730)
ISBN: 978-3-319-41500-0
ISBN: 978-3-319-41501-7
International Conference on Image Analysis and Recognition (ICIAR) <13, 2016, Póvoa de Varzim>
Fraunhofer IGD ()
biometric; handwriting identification; image analysis; CRISP; alignment; segmentation; feature extraction; classification; biometric fusion; Guiding Theme: Digitized Work; Guiding Theme: Smart City; Research Area: Computer vision (CV)

When using anonymous offline questionnaires for reviewing services or products it is often not guaranteed that a reviewer does this only once as intended. In this paper an applied combination of different features of handwritten characteristics and its fusion is presented to expose such manipulations. The presented approach covers the aspects of alignment normalization, segmentation, feature extraction, classification and fusion. Nine features from handwritten text, numbers and checkboxes are extracted and used to recognize hand-writer duplicates. The proposed method has been tested on a novel database containing pages of handwritten text produced by 1,734 writers. Furthermore we show that the unified biometric decision using a weighted sum combination rule can significantly improve writer identification performance even on low level features.