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A SELF-LEARNING SYSTEM AND METHODS FOR AUTOMATIC DOCUMENT RECOGNITION, AUTHENTICATION, AND INFORMATION EXTRACTION

  • US 20170132866A1
  • Filed: 06/19/2015
  • Published: 05/11/2017
  • Est. Priority Date: 06/20/2014
  • Status: Active Grant
First Claim
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1. ) A computerized method for developing a pairwise comparison nodal network for classification of an item capable of authentication comprising the steps of:

  • receiving electronic representations of a plurality of items capable of authentication of a first item class, a plurality of items capable of authentication of a second item class, and a plurality of items capable of authentication of a third item class, by at least one computer;

    selecting, by the at least one computer, a plurality of regions of the scanned representation of the plurality of items, the plurality of regions being the same for each item of each of the first, second, and third item class;

    recording, by the at least one computer, at least one measurement for each of the plurality of regions relating to the scanned representation of each of the plurality of items in each of the first, second, and third item class;

    compiling and storing the measurements of the plurality of items of the first item class as a first feature vector by the at least one computer to a computerized storage;

    compiling and storing the measurements of the plurality of items of the second item class as a second feature vector by the at least one computer to the computerized storage;

    compiling and storing the measurements of the plurality of items of the third item class as a third feature vector by the at least one computer to the computerized storage;

    comparing, by the at least one computer, the first feature vector with the second feature vector, the comparing step comprising identifying a plurality of the measurements having the greatest difference between the first feature vector and second feature vector, the identified plurality of measurements being the best distinguishing measurements between the first item class and second item class;

    compiling and storing the identified plurality of the measurements having the greatest difference between the first feature vector and second feature vector as a first inter class feature vector by the at least one computer to the computerized storage;

    comparing, by the at least one computer, the first feature vector with the third feature vector, the comparing step comprising identifying a second plurality of the measurements having the greatest difference between the first feature vector and third feature vector;

    compiling and storing the second plurality of the measurements having the greatest difference between the first feature vector and third feature vector as a second inter class feature vector by the at least one computer to the computerized storage;

    comparing, by the at least one computer, the second feature vector with the third feature vector, the comparing step comprising identifying a third plurality of the measurements having the greatest difference between the second feature vector and third feature vector;

    compiling and storing the third plurality of the measurements having the greatest difference between the second feature vector and third feature vector as a third inter class feature vector by the at least one computer to the computerized storage;

    forming, by the at least one computer, a pairwise comparison nodal network having a first comparison node comprising the first interclass feature vector, a second comparison node comprising the second interclass feature vector, and a third comparison node comprising the third interclass feature vector, wherein the pairwise comparison nodal network is usable by the at least one computer to compare interclass feature vector measurements of each node in a pairwise fashion, a result of an analysis by the pairwise comparison nodal network providing an identification of what class a candidate item being classified best corresponds to.

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