IP Library Granted Patent US 10,706,320
Granted Patent B2
US 10,706,320 · App. 16/175,364 · Granted Jul 7, 2020

Determining a document type of a digital document

Inventor: Irina Zosimovna Filimonova (Moscow, RU)
Assignee: ABBYY Production LLC
G06K9/6202G06K9/00442G06K9/00469G06K9/46G06K9/626G06K9/628G06K9/6262G06K9/6281G06K2209/01
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Quick Facts
Patent No.
US 10,706,320
App. No.
16/175,364
Granted
Jul 7, 2020
Kind
B2
Abstract

Disclosed are systems and method for determining document type of a digital document. An example method comprises: executing a first MLA classifier in order to determine a document type for a digital document, wherein the first MLA classifier is associated with a first hierarchical order of execution, and wherein the first MLA classifier is trained on a first trained dataset containing a first document type and a second document type, wherein the first document type is confidently predictable by the first MLA classifier and the second document type is not confidently predictable by the first MLA classifier; and responsive to determining that the first MLA classifier produced the second document type for the digital document, executing a second MLA classifier in order to determine the document type for the digital document, wherein the second MLA classifier is associated with a second hierarchical order of execution following the first hierarchical order of execution, and wherein the second MLA classifier is trained on a second trained dataset containing no documents of the first document type.

Claims (45)

1. A method, comprising:

executing, by a processor, a first machine learning algorithm (MLA) classifier in order to determine a document type for a digital document, wherein the first MLA classifier is associated with a first hierarchical order of execution, and wherein the first MLA classifier is trained on a first trained dataset containing a first document type and a second document type, wherein the first document type is confidently predictable by the first MLA classifier and the second document type is not confidently predictable by the first MLA classifier; and

responsive to determining that the first MLA classifier produced the second document type for the digital document, executing a second MLA classifier in order to determine the document type for the digital document, wherein the second MLA classifier is associated with a second hierarchical order of execution following the first hierarchical order of execution, and wherein the second MLA classifier is trained on a second trained dataset containing no documents of the first document type.

2. The method of claim 1 , further comprising:

responsive to determining that the first MLA classifier produced the first document type for the digital document, assigning the first document type to the digital document.

3. The method of claim 1 , further comprising:

determining that the second MLA classifier produced the second document type for the digital document, wherein the second document type is confidently predictable by the second MLA classifier; and

assigning the second document type to the digital document.

4. The method of claim 1 , further comprising:

determining that the second MLA classifier produced the second document type for the digital document, wherein the second document type is not confidently predictable by the second MLA classifier; and

executing a third MLA classifier in order to determine the document type for the digital document, wherein the third MLA classifier is associated with a third hierarchical order of execution following the second hierarchical order of execution.

5. The method of claim 1 , wherein the first MLA classifier is provided by at least one of: a raster-based classifier, a logotype-based classifier, a text-based classifier, or a rule-based classifier.

6. The method of claim 1 , wherein the first document type is associated with a confidence parameter which is above a pre-determined threshold.

7. The method of claim 1 , wherein the first document type is associated with a confidence parameter, and wherein a difference between the confidence parameter and a next-document-type hypothesis confidence parameter is above a pre-determined threshold.

8. The method of claim 1 , further comprising:

based on the document type, executing a computer-executable action with respect to the digital document.

9. A system, comprising:

a memory;

a processor coupled to the memory, wherein the processor is configured to:

execute a first machine learning algorithm (MLA) classifier in order to determine a document type for a digital document, wherein the first MLA classifier is associated with a first hierarchical order of execution, and wherein the first MLA classifier is trained on a first trained dataset containing a first document type and a second document type, wherein the first document type is confidently predictable by the first MLA classifier and the second document type is not confidently predictable by the first MLA classifier; and

responsive to determining that the first MLA classifier produced the second document type for the digital document, execute a second MLA classifier in order to determine the document type for the digital document, wherein the second MLA classifier is associated with a second hierarchical order of execution following the first hierarchical order of execution, and wherein the second MLA classifier is trained on a second trained dataset containing no documents of the first document type.

10. The system of claim 9 , wherein the processor is further configured to:

responsive to determining that the first MLA classifier produced the first document type for the digital document, assign the first document type to the digital document.

11. The system of claim 9 , wherein the processor is further configured to:

determining that the second MLA classifier produced the second document type for the digital document, wherein the second document type is confidently predictable by the second MLA classifier; and

assign the second document type to the digital document.

12. The system of claim 9 , wherein the processor is further configured to:

determine that the second MLA classifier produced the second document type for the digital document, wherein the second document type is not confidently predictable by the second MLA classifier; and

execute a third MLA classifier in order to determine the document type for the digital document, wherein the third MLA classifier is associated with a third hierarchical order of execution following the second hierarchical order of execution.

13. The system of claim 9 , wherein the first MLA classifier is provided by at least one of: a raster-based classifier, a logotype-based classifier, a text-based classifier, or a rule-based classifier.

14. The system of claim 9 , wherein the first document type is associated with a confidence parameter which is above a pre-determined threshold.

15. The system of claim 9 , wherein the first document type is associated with a confidence parameter, and wherein a difference between the confidence parameter and a next-document-type hypothesis confidence parameter is above a pre-determined threshold.

16. The system of claim 9 , wherein the processor is further configured to:

based on the document type, execute a computer-executable action with respect to the digital document.

17. A computer-readable non-transitory storage medium comprising executable instructions that, when executed by a computer system, cause the computer system to:

execute a first machine learning algorithm (MLA) classifier in order to determine a document type for a digital document, wherein the first MLA classifier is associated with a first hierarchical order of execution, and wherein the first MLA classifier is trained on a first trained dataset containing a first document type and a second document type, wherein the first document type is confidently predictable by the first MLA classifier and the second document type is not confidently predictable by the first MLA classifier; and

responsive to determining that the first MLA classifier produced the second document type for the digital document, execute a second MLA classifier in order to determine the document type for the digital document, wherein the second MLA classifier is associated with a second hierarchical order of execution following the first hierarchical order of execution, and wherein the second MLA classifier is trained on a second trained dataset containing no documents of the first document type.

18. The computer-readable non-transitory storage medium of claim 17 , further comprising executable instructions causing the computer system to:

responsive to determining that the first MLA classifier produced the first document type for the digital document, assigning the first document type to the digital document.

19. The computer-readable non-transitory storage medium of claim 17 , further comprising executable instructions causing the computer system to:

determining that the second MLA classifier produced the second document type for the digital document, wherein the second document type is confidently predictable by the second MLA classifier; and

assigning the second document type to the digital document.

20. The computer-readable non-transitory storage medium of claim 17 , further comprising executable instructions causing the computer system to:

determining that the second MLA classifier produced the second document type for the digital document, wherein the second document type is not confidently predictable by the second MLA classifier; and

executing a third MLA classifier in order to determine the document type for the digital document, wherein the third MLA classifier is associated with a third hierarchical order of execution following the second hierarchical order of execution.

Assignments (4)
SECURITY INTEREST Recorded Aug 14, 2023
From: ABBYY INC.; ABBYY USA SOFTWARE HOUSE INC.; ABBYY DEVELOPMENT INC.
To: WELLS FARGO BANK, NATIONAL ASSOCIATION, AS AGENT
Reel/Frame 064730/0964 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 25, 2022
From: ABBYY PRODUCTION LLC
To: ABBYY DEVELOPMENT INC.
Reel/Frame 059249/0873 →
MERGER Recorded Jan 24, 2019
From: ABBYY DEVELOPMENT LLC
To: ABBYY PRODUCTION LLC
Reel/Frame 048129/0558 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 30, 2018
From: FILIMONOVA, IRINA ZOSIMOVNA
To: ABBYY DEVELOPMENT LLC
Reel/Frame 047360/0907 →
Cited By (1)
US 12,525,000