Automated document processing for detecting, extracting, and analyzing tables and tabular data

1. A computer-implemented method for detecting and classifying tables and/or tabular data arrangements within an original image, comprising:

pre-processing the original image to generate processed image data;

detecting one or more tables and/or one or more tabular data arrangements within the processed image data;

extracting the one or more tables and/or the one or more tabular data arrangements from the processed image data; and

classifying either:

the one or more extracted tables;

portions of the one or more extracted tables;

the one or more extracted tabular data arrangements;

portions of the one or more extracted tabular data arrangements; or

a combination of:

the one or more extracted tables;

the portions of the one or more extracted tables;

the one or more extracted tabular data arrangements; and/or

the portions of the one or more extracted tabular data arrangements; and

wherein classifying the one or more extracted tables; the portions of the one or more extracted tables; the one or more extracted tabular data arrangements; and/or the portions of the one or more extracted tabular data arrangements comprises:

evaluating a test dataset using a trained classification model;

determining at least one score vector based on the evaluation;

identifying a highest score within the at least one score vector;

determining whether the highest score for the at least one score vector is greater than a corresponding one of a plurality of optimized score thresholds; and

in response to determining the highest score for the at least one score vector is greater than the corresponding one of the plurality of optimized score thresholds, returning a positive result for the one of the corresponding classification of the one or more extracted tables; the portions of the one or more extracted tables; the one or more extracted tabular data arrangements; and/or the portions of the one or more extracted tabular data arrangements.

2. The method as recited in claim 1 , wherein the one or more extracted tables, the portions of the one or more extracted tables, the one or more extracted tabular data arrangements, and/or the portions of the one or more extracted tabular data arrangements collectively comprise:

one or more classifications of interest; and

at least one classification not of interest.

3. The method as recited in claim 1 , wherein the classification of: the one or more extracted tables; the portions of the one or more extracted tables; the one or more extracted tabular data arrangements; and/or the portions of the one or more extracted tabular data arrangements are each independently based on tables and/or tabular data arrangements represented in a training dataset.

4. The method as recited in claim 1 , wherein the classifying includes training a machine learning model, wherein the training comprises training the machine learning model to recognize a plurality of relevancy criteria, and wherein each relevancy criterion is independently indicative of a given one of the one or more tables and/or a given one of the one or more tabular data arrangements corresponding to either:

one of the one or more table classifications;

one of the one or more tabular data arrangement classifications; or

one of the one or more column classifications.

5. The method as recited in claim 4 , wherein the plurality of relevancy criteria comprise:

a frequency of one or more terms represented in the one or more tables and/or the one or more tabular data arrangements;

a term-frequency/inverse-document frequency (tf-idf) corresponding to the one or more terms and one or more documents representing the one or more tables and/or the one or more tabular data arrangements;

a structure of a sub-region of the one or more tables and/or the one or more tabular data arrangements; and/or

structured information describing some or all of the one or more tables and/or the one or more tabular data arrangements.

6. The method as recited in claim 1 , comprising training at least one machine learning model, using a training dataset representing a plurality of different types of tables and/or tabular data arrangements, based at least in part on:

a plurality of recognized textual elements within the training dataset; and

a plurality of recognized regions and/or subregions of the different types of tables and/or tabular data arrangements represented by the training set.

7. The method as recited in claim 6 , wherein the training comprises generating a score matrix comprising a plurality of score vectors that each independently comprise a plurality of scores for a single table or tabular data arrangement represented in the training dataset; and wherein each of the plurality of scores for each score vector independently corresponds to a possible classification of the single table or tabular data arrangement represented in the training dataset.

8. The method as recited in claim 6 , further comprising:

associating a known classification type with each score vector of the score matrix; and

identifying an optimal score threshold for each known classification type.

9. A computer-implemented method for classifying one or more tables and/or one or more tabular data arrangements represented within a test dataset, the method comprising:

using a trained classification model to classify the one or more tables and/or one or more tabular data arrangements represented within a test dataset according to:

one or more table classifications;

one or more tabular data arrangement classifications; and/or

one or more column classifications; and

wherein classifying the one or more tables and/or the one or more tabular data arrangements comprises:

evaluating the test dataset using the trained classification model;

determining at least one score vector based on the evaluation;

identifying a highest score within the at least one score vector;

determining whether the highest score for the at least one score vector is greater than a corresponding one of a plurality of optimized score thresholds; and

in response to determining the highest score for the at least one score vector is greater than the corresponding one of the plurality of optimized score thresholds, returning a positive result for the one of the corresponding classification of the one or more tables and/or the one or more extracted tabular data arrangements.

10. The method as recited in claim 9 , comprising training a machine learning model, using a training dataset representing a plurality of different tables and/or tabular data arrangements, based at least in part on a plurality of recognized textual elements within the training dataset;

wherein training the machine learning model comprises training the machine learning model to:

recognize the textual elements within the training dataset; and

understand a structure of the different tables and/or tabular data arrangements.

11. The method as recited in claim 10 , wherein the training is further based at least in part on a structure of the one or more tables and/or the one or more tabular data arrangements.

12. The method as recited in claim 10 , wherein the training is further based at least in part on a plurality of recognized regions and/or subregions of the different types of tables and/or tabular data arrangements represented by the training set.

13. The method as recited in claim 10 , wherein the training comprises generating a score matrix comprising a plurality of score vectors that each independently comprise a plurality of scores for a single table or tabular data arrangement represented in the training dataset; and wherein each of the plurality of scores for each score vector independently corresponds to a possible classification of the single table or tabular data arrangement represented in the training dataset.

14. The method as recited in claim 13 , further comprising:

associating a known classification type with each score vector of the score matrix; and

identifying an optimal score threshold for each classification type.

15. The method as recited in claim 9 , wherein the one or more table classifications, the one or more tabular data arrangement classifications, and/or the one or more column classifications collectively comprise:

one or more classifications of interest; and

at least one classification not of interest.

16. The method as recited in claim 9 , wherein the one or more table classifications, the one or more tabular data arrangement classifications, and the one or more column classifications are each independently based on the different tables and/or the tabular data arrangements represented in the training dataset.

17. The method as recited in claim 9 , wherein the trained classification model is adapted to recognize a plurality of relevancy criteria, wherein each relevancy criterion is independently indicative of a given one of the one or more tables and/or a given one of the one or more tabular data arrangements corresponding to either:

one of the one or more table classifications;

one of the one or more tabular data arrangement classifications; or

one of the one or more column classifications.

18. The method as recited in claim 17 , wherein the plurality of relevancy criteria comprise:

a frequency of one or more terms represented in the one or more tables and/or the one or more tabular data arrangements;

a term-frequency/inverse-document frequency (tf-idf) corresponding to the one or more terms and one or more documents representing the one or more tables and/or the one or more tabular data arrangements;

a structure of a sub-region of the one or more tables and/or the one or more tabular data arrangements; and/or

structured information describing some or all of the one or more tables and/or the one or more tabular data arrangements.