A new document authentication method by embedding deformation characters

Document authentication decides whether a given document is from a specific individual or not. In this paper, we propose a new document authentication method in physical (after document printed out) domain by embedding deformation characters. When an author writers a document to a specific individual or organization, a unique error-correcting code which serves as his Personal Identification Number (PIN) is proposed and then some characters in the text line are deformed according to his PIN. By doing so, the writer's personal information is embedded in the document. When the document is received, it is first scanned and recognized by an OCR module, and then the deformed characters are detected to get the PIN, which can be used to decide the originality of the document. So the document authentication can be viewed as a kind of communication problems in which the identity of a document from a writer is being "transmitted" over a channel. The channel consists of the writer's PIN, the document, and the encoding rule. Experimental result on deformation character detection is very promising, and the availability and practicability of the proposed method is verified by a practical system.

[1]  Xiaoqing Ding,et al.  Writer identification using directional element features and linear transform , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[2]  D. Cox,et al.  An Analysis of Transformations , 1964 .

[3]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[4]  Min Wu,et al.  Data hiding in binary image for authentication and annotation , 2004, IEEE Transactions on Multimedia.

[5]  Dragana Brzakovic,et al.  Document recognition/authentication based on medium-embedded random patterns , 1993, Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93).

[6]  Thomas G. Dietterich,et al.  Solving Multiclass Learning Problems via Error-Correcting Output Codes , 1994, J. Artif. Intell. Res..

[7]  Nei Kato,et al.  A Handwritten Character Recognition System Using Directional Element Feature and Asymmetric Mahalanobis Distance , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Robert H. Deng,et al.  An optical watermarking solution for authenticating printed documents , 2001, Proceedings International Conference on Information Technology: Coding and Computing.

[9]  Steven H. Low,et al.  Marking text documents , 1997, Proceedings of International Conference on Image Processing.

[10]  J. Friedman Regularized Discriminant Analysis , 1989 .

[11]  Changsong Liu,et al.  Multi-scale feature extraction and nested-subset classifier design for high accuracy handwritten character recognition , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.