Signature Segmentation from Document Images

In this paper we propose a novel method for the extraction of signatures from document images. Instead of using a human defined set of features a part-based feature extraction method is used. In particular, we use the Speeded Up Robust Features (SURF) to distinguish the machine printed text from sig...

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Bibliographic Details
Published in2012 International Conference on Frontiers in Handwriting Recognition pp. 425 - 429
Main Authors Ahmed, S., Malik, M. I., Liwicki, M., Dengel, A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2012
Subjects
extraction
Feature extraction
Handwriting recognition
Image segmentation
local features
logos
machine printed text
Robustness
Signature segmentation
SURF
Text analysis
Training
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ISBN9781467322621
1467322628
DOI10.1109/ICFHR.2012.271

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Summary:In this paper we propose a novel method for the extraction of signatures from document images. Instead of using a human defined set of features a part-based feature extraction method is used. In particular, we use the Speeded Up Robust Features (SURF) to distinguish the machine printed text from signatures. Using SURF features makes the approach generally more useful and reliable for different resolution documents. We have evaluated our system on the publicly available Tobacco-800 dataset in order to compare it to previous work. Finally, all signatures were found in the images and less than half of the found signatures are false positives. Therefore, our system can be applied for practical use.
ISBN:9781467322621
1467322628
DOI:10.1109/ICFHR.2012.271