SYSTEMS AND METHODS FOR MACHINE LEARNING KEY-VALUE EXTRACTION ON DOCUMENTS

1 . A method to improve, post-extraction, classification accuracy of key-values after a machine-learning model has been applied to documents, the method comprising:

receiving a collection of document images;

creating an input data set from the collection;

applying a classification model to the input data set that generates an initial set of entity predictions; and

filtering the initial set of entity predictions that generates a revised set of entity predictions, wherein the filtering the initial set of entity predictions further comprises applying at least a plurality of rules to the initial set of entity predictions, the plurality of rules comprising:

a first rule corresponding to treating each individual entity as unique, and

a second rule corresponding to treating a single document as unique.

2 . The method of claim 1 , wherein the plurality of rules further comprise:

a third rule corresponding to a threshold level for entity probabilities.

3 . The method of claim 2 , wherein the plurality of rules further comprise:

a fourth rule corresponding to selection of an entity with a highest probability for each class.

4 . The method of claim 3 , wherein the plurality of rules further comprise:

a fifth rule corresponding to prioritizing entity selection of a largest multi-gram from a plurality of multi-gram candidates.

5 . The method of claim 1 , wherein the creating the input data set from the collection further comprises:

applying optical character recognition to a first document image of the collection of document images,

wherein the applying the optical character recognition outputs a plurality of document objects, the input data set comprising the plurality of document objects.

6 . The method of claim 5 , wherein a document object of the plurality of document objects comprises a line object with multiple unigrams on a same line with a distance between adjacent unigrams less than a value.

7 . A computer program product, comprising: a computer readable storage medium having stored thereon computer readable program instructions executable by one or more processors to cause the one or more processors to:

receive a collection of document images;

create an input data set from the collection;

apply a classification model to the input data set that generates an initial set of entity predictions; and

filter the initial set of entity predictions that generates a revised set of entity predictions, wherein the filtering the initial set of entity predictions further comprises applying at least a plurality of rules to the initial set of entity predictions, the plurality of rules comprising:

a first rule corresponding to treating each individual entity as unique, and

a second rule corresponding to treating a single document as unique.

8 . The computer program product of claim 7 , wherein the plurality of rules further comprise:

a third rule corresponding to a threshold level for entity probabilities.

9 . The computer program product of claim 8 , wherein the plurality of rules further comprise:

a fourth rule corresponding to selection of an entity with a highest probability for each class.

10 . The computer program product of claim 9 , wherein the plurality of rules further comprise:

a fifth rule corresponding to prioritizing entity selection of a largest multi-gram from a plurality of multi-gram candidates.

11 . The computer program product of claim 7 , wherein the creating the input data set from the collection further comprises:

applying optical character recognition to a first document image of the collection of document images,

wherein the applying the optical character recognition outputs a plurality of document objects, the input data set comprising the plurality of document objects.

12 . The computer program product of claim 11 , wherein a document object of the plurality of document objects comprises a line object with multiple unigrams on a same line with a distance between adjacent unigrams less than a value.

13 . A system comprising:

a data store configured to store computer-executable instructions; and

a hardware processor in communication with the data store, the hardware processor, when executing the computer-executable instructions, is configured to:

receive a collection of document images;

create an input data set from the collection;

apply a classification model to the input data set that generates an initial set of entity predictions; and

filter the initial set of entity predictions that generates a revised set of entity predictions, wherein the filtering the initial set of entity predictions further comprises applying at least a plurality of rules to the initial set of entity predictions, the plurality of rules comprising:

a first rule corresponding to treating each individual entity as unique, and

a second rule corresponding to treating a single document as unique.

14 . The system of claim 13 , wherein the plurality of rules further comprise:

a third rule corresponding to a threshold level for entity probabilities.

15 . The system of claim 14 , wherein the plurality of rules further comprise:

a fourth rule corresponding to selection of an entity with a highest probability for each class.

16 . The system of claim 15 , wherein the plurality of rules further comprise:

a fifth rule corresponding to prioritizing entity selection of a largest multi-gram from a plurality of multi-gram candidates.

17 . The system of claim 13 , wherein the creating the input data set from the collection further comprises:

applying optical character recognition to a first document image of the collection of document images,

wherein the applying the optical character recognition outputs a plurality of document objects, the input data set comprising the plurality of document objects.

18 . The system of claim 17 , wherein a document object of the plurality of document objects comprises a line object with multiple unigrams on a same line with a distance between adjacent unigrams less than a value.