Document inspection using text-line alignment

Passports, ID cards, banknotes, and degrees are considered as valuable documents that need to be secured against forgery. Apart from those, there are many other document types that are valuable, too, but that do not have any security features, as e.g. bills and vouchers. These may be used by fraudulent people to defraud money from e.g. a car insurance company. The wide availability of scanning and printing hardware allows even non-experts to easily forge a document. We therefore present a new aspect in the examination of intrinsic document features for optical document security: the goal is to automatically detect text-lines that have been manipulated or additionally inserted in a document by inspecting their alignment (left, right or center) with respect to the other text-lines in the document. This constitutes an additional feature in the goal of developing a powerful toolbox for automatic document inspection. Using the extracted text-lines, the alignment margins are extracted. Statistics on the distances of the text-lines to the alignment margins are used to identify lines that might have been forged. Such documents can then be presented to a human operator for further inspection. Due to lack of public datasets containing forged documents, a new dataset had to be created. Evaluation showed a classification accuracy of 90.5%.

[1]  Thomas M. Breuel Robust least-square-baseline finding using a branch and bound algorithm , 2001, IS&T/SPIE Electronic Imaging.

[2]  Thomas M. Breuel,et al.  Automatic Line Orientation Measurement for Questioned Document Examination , 2009, IWCF.

[3]  Isaac Amidror New print-based security strategy for the protection of valuable documents and products using moire intensity profiles , 2002, IS&T/SPIE Electronic Imaging.

[4]  Thomas M. Breuel On the use of interval arithmetic in geometric branch and bound algorithms , 2003, Pattern Recognit. Lett..

[5]  Thomas M. Breuel,et al.  Performance Evaluation and Benchmarking of Six-Page Segmentation Algorithms , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Thomas M. Breuel,et al.  Document Signature Using Intrinsic Features for Counterfeit Detection , 2008, IWCF.

[7]  Thomas M. Breuel,et al.  Combined orientation and skew detection using geometric text-line modeling , 2009, International Journal on Document Analysis and Recognition (IJDAR).

[8]  Matti Pietikäinen,et al.  Adaptive document image binarization , 2000, Pattern Recognit..

[9]  Carlos Luna,et al.  Commercial anticounterfeit products using machine vision , 2004, IS&T/SPIE Electronic Imaging.

[10]  Thomas M. Breuel,et al.  Resolution independent skew and orientation detection for document images , 2009, Electronic Imaging.

[11]  Martin Neebe,et al.  Multifunctional optical security features based on bacteriorhodopsin , 2004, IS&T/SPIE Electronic Imaging.

[12]  R. L. van Renesse,et al.  Paper based document security-a review , 1997 .

[13]  Thomas M. Breuel,et al.  Evaluation of Graylevel-Features for Printing Technique Classification in High-Throughput Document Management Systems , 2008, IWCF.