System and method of using dynamic variance networks

• US 9,213,756 B2
• Filed: 11/02/2009
• Issued: 12/15/2015
• Est. Priority Date: 11/02/2009
• Status: Active Grant

• Current Assignee: Hyland Switzerland, Sarl (HSI Holdings II Incorporated)
• Original Assignee: Johannes Hausmann, Ralph Meier, Thorsten Wanschura
• Inventors: Urbschat, Harry, Meier, Ralph, Wanschura, Thorsten, Hausmann, Johannes
• Primary Examiner(s): CHEEMA, AZAM M
• Application Number:
  US12/610,915
• Publication Number:
  US 20110106823A1
• Time in Patent Office: 2,234 Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/30 of unstructured textual data document management systems G06F16/93
• CPC Class Codes: G06F 18/2411 based on the proximity to a decision surface, e.g. support vector machines
• CPC Class Codes: G06V 30/10 Character recognition
• CPC Class Codes: G06V 30/12 Detection or correction of errors, e.g. by rescanning the pattern
• CPC Class Codes: G06V 30/1452 based on positionally close symbols, e.g. amount sign or URL-specific characters
• CPC Class Codes: G06V 30/416 Extracting the logical structure, e.g. chapters, sections or page numbers; Identifying elements of the document, e.g. authors

Abstract

Systems and methods for determining a location of a target in a document. Information compiled from a training document is created, the information comprising a reference and a reference vector tying each reference to the target. The reference is compared to a new reference in a new document to determine if there are any similar references that are the target, wherein similar references are: position similar, or type similar, or both. When the new reference comprises a typo, an optical character recognition (OCR) mistake, or both, the new reference is still determined to be the target because of the new reference'"'"'s location.

Claims

1. A method for determining at least one location of at least one target in at least one document, comprising:

creating, utilizing at least one localization module and at least one processor, information compiled from at least one training document, the information comprising at least one reference and at least one reference vector tying each reference to the at least one target, wherein the creating further comprises;

finding, utilizing the at least one localization module, the at least one reference;

creating, utilizing the at least one localization module, the at least one reference vector for each reference;

performing variance filtering, utilizing the at least one localization module, on the at least one reference and the at least one reference vector from each document to obtain any similar references and any similar reference vectors from all documents; and

using any similar references and any similar reference vectors, utilizing the at least one localization module, to create at least one dynamic variance network (DVN), the at least one DVN comprising at least one reference and at least one reference vector tying each reference to the at least one target;

comparing, utilizing the at least one localization module, the at least one reference to at least one new reference in at least one new document to determine if there are any similar references that are the at least one target, wherein similar references are;

position similar, or type similar, or both; and

wherein when the at least one new reference comprises at least one typo, at least one optical character recognition (OCR) mistake, or both, the at least one new reference is still determined to be the at least one target because of the at least one new reference'"'"'s location; and

applying the information, utilizing the at least one localization module, on at least one new document to determine at least one location of the at least one target on the at least one new document, wherein the applying further comprises;

comparing, utilizing the at least one localization module, any similar references to the at least one new reference on at least one new document to determine if there are any matching references; and

using, utilizing the at least one localization module, any similar reference vectors corresponding to any matching references to determine the at least one target on the at least one new document.

2. The method of claim 1, wherein the at least one reference and/or the at least one new reference comprises:

at least one character string,

at least one word;

at least one number;

at least one alpha-numeric representation;

at least one token;

at least one blank space;

at least one logo;

or

at least one text fragment;

or

any combination thereof.

3. The method of claim 1, wherein the at least one location of the at least one target is used to obtain information, or confirm information, or both, about the at least one target.

4. The method of claim 1, wherein similar reference vectors are also content similar.

5. The method of claim 1, wherein similarity across references and reference vectors is configurable.

6. The method of claim 5, wherein characteristics of the at least one reference are taken into account, the characteristics comprising:

font;

font size;

or style;

or any combination thereof.

7. The method of claim 1, wherein strict matching, or fuzzy matching, or both are utilized to match any similar references to the at least one new reference in the at least one new document.

8. The method of claim 1, wherein the at least one reference and/or the at least one new reference is:

merged with at least one other reference;

or split into at least two references;

or both.

9. The method of Claim 1, wherein the at least one DVN is dynamically adapted during document processing.

10. The method of Claim 1, wherein the at least one DVN is used for:

reference correction;

document classification;

page separation;

recognition of document modification;

document summarization;

or

document compression;

or any combination thereof.

11. The method of claim 1, wherein when the at least one new reference comprises an alternate spelling, the at least one new reference is still used as a reference because of the at least one new reference'"'"'s location.

12. A system for determining at least one location of at least one target in at least one document, comprising:

at least one processor, wherein the at least one processor is configured for;

creating, utilizing at least one localization module in communication with the at least one processor, information compiled from at least one training document, the information comprising at least one reference and at least one reference vector tying each reference to the at least one target, wherein the creating further comprises;

finding, utilizing the at least one localization module, the at least one reference;

creating, utilizing the at least one localization module, the at least one reference vector for each reference;

performing variance filtering, utilizing the at least one localization module, on the at least one reference and the at least one reference vector from each document to obtain any similar references and any similar reference vectors from all documents; and

using any similar references and any similar reference vectors, utilizing the at least one localization module, to create at least one dynamic variance network (DVN), the at least one DVN comprising at least one reference and at least one reference vector tying each reference to the at least one target;

comparing, utilizing the at least one localization module, the at least one reference to at least one new reference in at least one new document to determine if there are any similar references that are possibly the at least one target, wherein similar references are;

position similar, or type similar, or both; and

wherein when the at least one new reference comprises at least one typo, at least one OCR mistake, or any combination thereof, the at least one new reference is determined to be the at least one target because of the at least one new reference'"'"'s location; and

applying the information, utilizing the at least one localization module, on at least one new document to determine at least one location of the at least one target on the at least one new document, wherein the applying further comprises;

comparing, utilizing the at least one localization module, any similar references to the at least one new reference on at least one new document to determine if there are any matching references; and

using, utilizing the at least one localization module, any similar reference vectors corresponding to any matching references to determine the at least one target on the at least one new document.

13. The system of claim 12, wherein the at least one reference and/or the at least one new reference comprises:

at least one character string,

at least one word;

at least one number;

at least one alpha-numeric representation;

at least one token;

at least one blank space;

at least one logo;

or

at least one text fragment;

or

any combination thereof.

14. The system of claim 12, wherein the at least one location of the at least one target is used to obtain information, or confirm information, or both, about the target.

15. The system of claim 12, wherein similar reference vectors are also content similar.

16. The system of claim 12, wherein similarity across references and reference vectors is configurable.

17. The system of claim 16, wherein characteristics of the at least one reference are taken into account, the characteristics comprising:

font;

font size;

or style;

or any combination thereof.

18. The system of claim 12, wherein strict matching, or fuzzy matching, or both, are utilized to match any similar references to the at least one new reference in the at least one new document.

19. The system of claim 12, wherein the at least one reference and/or the at least one new reference is:

merged with at least one other reference;

or split into at least two references;

or both.

20. The system of claim 12, wherein the at least one DVN is dynamically adapted during document processing.

21. The system of claim 12, wherein the at least one DVN is used for:

reference correction;

document classification;

page separation;

recognition of document modification;

document summarization;

or

document compression;

or any combination thereof.

22. The system of claim 12, wherein when the at least one new reference comprises an alternate spelling, the at least one new reference is still used as a reference because of the at least one new reference'"'"'s location.