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Combining Low-Level Features of Offline Questionnaires for Handwriting Identification

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Image Analysis and Recognition (ICIAR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9730))

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Abstract

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.

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Correspondence to Dirk Siegmund .

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Siegmund, D., Ebert, T., Damer, N. (2016). Combining Low-Level Features of Offline Questionnaires for Handwriting Identification. In: Campilho, A., Karray, F. (eds) Image Analysis and Recognition. ICIAR 2016. Lecture Notes in Computer Science(), vol 9730. Springer, Cham. https://doi.org/10.1007/978-3-319-41501-7_6

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  • DOI: https://doi.org/10.1007/978-3-319-41501-7_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41500-0

  • Online ISBN: 978-3-319-41501-7

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