Platform for document classification

• US 10,503,971 B1
• Filed: 08/14/2019
• Issued: 12/10/2019
• Est. Priority Date: 03/19/2019
• Status: Active Grant

• Current Assignee: Capital One Services LLC (Capital One Financial Corporation)
• Original Assignee: Capital One Services LLC (Capital One Financial Corporation)
• Inventors: Dang, Steven, Gould, Jason, Jiang, Jennifer, Akatsuka, Christopher, Slattery, Douglas, Pasam, Vijaya
• Primary Examiner(s): Thomas, Mia M
• Application Number:
  US16/540,287
• Time in Patent Office: 118 Days
• CPC Class Codes: G06F 16/353 into predefined classes
• CPC Class Codes: G06F 16/93 Document management systems
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G06F 18/2431 Multiple classes
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06N 20/20 Ensemble learning
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 5/01 Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 30/413 Classification of content, e.g. text, photographs or tables

Abstract

A device obtains image data associated with a document. Using a first machine learning model, the device determines, for the document, a first classification of one of a plurality of document types and a first confidence score associated with the first classification, and a second classification of one of the plurality of document types and a second confidence score associated with the second classification based on the image data. The device determines a difference between the first confidence score and the second confidence score, compares the difference and a threshold value, and accept the first classification of the document when the difference satisfies the threshold value.

Claims

1. A method, comprising:

determining, by a processor, for a document, and using a first machine learning model, a first classification of a document type, of a plurality of document types, and a first confidence score associated with the first classification,

the first confidence score indicating a confidence level that the document type corresponds to the first classification;

comparing, by the processor, the first confidence score and a first threshold value;

selectively;

accepting, by the processor, the first classification of the document type when the first confidence score satisfies the first threshold value;

or

obtaining, by the processor, information associated with the document when the first confidence score fails to satisfy the first threshold value;

determining, by the processor, for the document, and using a second machine learning model, a second classification of the document type and a second confidence score associated with the second classification,

the second confidence score indicating a confidence level that the document type corresponds to the second classification;

comparing, by the processor, the second confidence score and a second threshold value; and

accepting, by the processor, the second classification of the document type when the second confidence score satisfies the second threshold value.

2. The method of claim 1, further comprising:

receiving a multiple-page document;

using a splitting technique to form multiple single-page documents from the multiple-page document; and

determining the document from the multiple single-page documents.

3. The method of claim 1, wherein the document contains at least two pages and the at least two pages contain different document types of the plurality of document types.

4. The method of claim 1, further comprising:

training the first machine learning model to determine the first confidence score based on historical data associated with a plurality of confidence scores of accurate document classifications, and

wherein the first confidence score is determined based on training the first machine learning model.

5. The method of claim 1, further comprising:

preprocessing the document by one or more of;

an image contrast adjustment,

an image noise filter,

an image histogram modification,

cropping an image of the document, or

a skew correction operation; and

wherein the second classification is determined based on the preprocessing.

6. The method of claim 1, further comprising:

training the second machine learning model using information that identifier a plurality of textual features,

the textual features includes one or more of;

words,

phrases, or

clauses; and

wherein the second classification is determined based on training the second machine learning model.

7. The method of claim 1, further comprising:

assigning a label after accepting the second classification of the document,

the label being one or more of;

a file name,

a tag,

a flag,

a location in directory system, or

an entry in a log or database.

8. A device, comprising:

a memory; and

one or more processors to;

determine, for a document, a first classification of a document type, of a plurality of document types, and a first confidence score associated with the first classification,

the first confidence score indicating a confidence level that the document type corresponds to the first classification;

compare the first confidence score and a first threshold value;

determine for the document, a second classification of the document type and a second confidence score associated with the second classification when the first confidence score fails to satisfy the first threshold value,

the second confidence score indicating a confidence level that the document type corresponds to the second classification;

compare the second confidence score and a second threshold value; and

accept the second classification of the document type when the second confidence score satisfies the second threshold value.

9. The device of claim 8, wherein the one or more processors are further to:

provide the document and information regarding the second classification to another device.

10. The device of claim 8, wherein the first classification is determined using one or more:

deep learning or

convolutional neural networks.

11. The device of claim 8, wherein the first confidence score is greater than the second confidence score.

12. The device of claim 8, wherein the first classification is one of:

a paystub,

a personal financial document, or

an income reporting form.

13. The device of claim 8, wherein the one or more processors are further to:

train a machine learning model to determine the first confidence score based on features of another document of a particular document type, and

wherein the first confidence score is determined based on the trained machine learning model.

14. The device of claim 8, wherein the one or more processors are further to:

train a machine learning model to use a computer vision technique to;

identify a plurality of features of the document, and

perform a dimensionality reduction to reduce the plurality of features to a particular feature set; and

wherein the first classification is determined based on the trained machine learning model.

15. A non-transitory computer-readable medium storing instructions, the instructions comprising:

one or more instructions that, when executed by one or more processors, cause the one or more processors to;

determine, for a document, a first classification of a document type, of a plurality of document types, and a first confidence score,

the first confidence score indicating a confidence level that the document corresponds to the first classification;

compare the first confidence score and a first threshold value;

selectively;

accept the first classification of the document type when the first confidence score satisfies the first threshold value;

or

obtain information associated with the document when the first confidence score fails to satisfy the first threshold value;

determine, for the document, a second classification of the document type and a second confidence score,

the second confidence score indicating a confidence level that the document corresponds to the second classification;

compare the second confidence score and a second threshold value; and

accept the second classification of the document type when the second confidence score satisfies the second threshold value.

16. The non-transitory computer-readable medium of claim 15, wherein the first classification is determined using one or more:

deep learning or

convolutional neural networks.

17. The non-transitory computer-readable medium of claim 15, wherein the first confidence score is greater than the second confidence score.

18. The non-transitory computer-readable medium of claim 15, wherein the one or more instructions when executed by the one or more processors, further cause the one or more processors to:

train a machine learning model to determine the first confidence score based on features of another document of a particular document type; and

wherein the first confidence score is determined based on the trained machine learning model.

19. The non-transitory computer-readable medium of claim 15, wherein the one or more instructions when executed by the one or more processors, further cause the one or more processors to:

train a machine learning model to use a computer vision technique to;

identify a plurality of features of the document, and

perform a dimensionality reduction to reduce the plurality of features to a particular feature set; and

wherein the first classification is determined based on the trained machine learning model.

20. The non-transitory computer-readable medium of claim 15, wherein the one or more instructions when executed by the one or more processors, further cause the one or more processors to:

receive a multiple-page document;

use a splitting technique to form multiple single-page documents from the multiple-page document; and

determine the document from the multiple single-page documents.