Implementation techniques for geometric branch-and-bound matching methods
Computer Vision and Image Understanding
Camera-Based Document Image Mosaicing
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
Performance Evaluation and Benchmarking of Six-Page Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fourier Preprocessing for Hand Print Character Recognition
IEEE Transactions on Computers
Page frame detection for marginal noise removal from scanned documents
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Image-matching for revision detection in printed historical documents
Proceedings of the 29th DAGM conference on Pattern recognition
Computational Forensics: An Overview
IWCF '08 Proceedings of the 2nd international workshop on Computational Forensics
Estimation of Inkjet Printer Spur Gear Teeth Number from Pitch Data String of Limited Length
IWCF '09 Proceedings of the 3rd International Workshop on Computational Forensics
Automatic Line Orientation Measurement for Questioned Document Examination
IWCF '09 Proceedings of the 3rd International Workshop on Computational Forensics
IWCF'10 Proceedings of the 4th international conference on Computational forensics
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Document security does not only play an important role in specific domains e.g. passports, checks and degrees but also in every day documents e.g. bills and vouchers. Using special high-security features for this class of documents is not feasible due to the cost and the complexity of these methods. We present an approach for detecting falsified documents using a document signature obtained from its intrinsic features: bounding boxes of connected components are used as a signature. Using the model signature learned from a set of original bills, our approach can identify documents whose signature significantly differs from the model signature. Our approach uses globally optimal document alignment to build a model signature that can be used to compute the probability of a new document being an original one. Preliminary evaluation shows that the method is able to reliably detect faked documents.