SYSTEM AND METHOD OF USING DYNAMIC VARIANCE NETWORKS

• US 20110106823A1
• Filed: 11/02/2009
• Published: 05/05/2011
• Est. Priority Date: 11/02/2009
• Status: Active Grant

• Current Assignee: Hyland Switzerland, Sarl
• Original Assignee: Brainware Incorporated (Ninestar Corporation)
• Inventors: Hausmann, Johannes, Meier, Ralph, Urbschat, Harry, Wanschura, Thorsten
• Granted Patent: US 9,213,756 B2
• Time in Patent Office: Days
• US Class Current: 707/754
• 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

A method and system for determining at least one location of at least one target in at least one document, comprising: creating, utilizing at least one localization module, 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.

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, 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.

2. The method of claim 1, further comprising:

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.

3. The method of claim 1, 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;

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.

4. The method of claim 3, wherein the applying further comprises:

comparing, utilizing the at least one localization module, any similar references to at least one 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.

5. The method of claim 1, wherein the at least one 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;

6. The method of claim 2, wherein the at least one location of the at least one target is used to obtain and/or confirm information about the target.

7. The method of claim 1, wherein the at least one reference comprises a typo, OCR mistake, or alternate spelling, or any combination thereof;

but can still be used as a reference because of the at least one reference'"'"'s location.

8. The method of claim 4, wherein similar reference vectors can be:

positionally similar;

content similar;

or type similar;

or any combination thereof.

9. The method of claim 3, wherein similarity across the references and the reference vectors is configurable.

10. The method of claim 3, wherein strict and/or fuzzy matching can be utilized to match any similar references to at least one reference in the at least one new document.

11. The method of claim 9, wherein the following characteristics of the at least one reference are taken into account:

font;

font size;

style;

or any combination thereof.

12. The method of claim 1, wherein the at least one reference is:

merged with at least one other reference; and

/or split into at least two references.

13. The method of claim 3, wherein the at least one DVN is dynamically adapted during document processing.

14. The method of claim 3, 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.

26. The system of claim 14, wherein the at least one reference is:

merged with at least one other reference; and

/or split into at least two references.

15. 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, 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.

16. The system of claim 15, wherein the at least one processor if further configured for:

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.

17. The system of claim 15, wherein the at least one processor is further configured for:

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;

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.

18. The system of claim 17, wherein the applying further comprises:

comparing, utilizing the at least one localization module, any similar references to at least one 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.

19. The system of claim 15, wherein the at least one 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;

20. The system of claim 15, wherein the at least one location of the at least one target is used to obtain and/or confirm information about the target.

21. The system of claim 15, wherein the at least one reference comprises a typo, OCR mistake, or alternate spelling, or any combination thereof;

but can still be used as a reference because of the at least one reference'"'"'s location.

22. The system of claim 18, wherein similar reference vectors can be:

positionally similar;

content similar;

or type similar;

or any combination thereof.

23. The system of claim 17, wherein similarity across the references and the reference vectors is configurable.

24. The system of claim 17, wherein strict and/or fuzzy matching can be utilized to match any similar references to at least one reference in the at least one new document.

25. The system of claim 23, wherein the following characteristics of the at least one reference are taken into account:

font;

font size;

style;

or any combination thereof.

27. The system of claim 17, wherein the at least one DVN is dynamically adapted during document processing.

28. The system of claim 17, 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.