Header retraining decision system

The method of claim 41, further comprising:

• combining an image with a header mask to generate the header image.

The method of claim 41, wherein executing the text extraction model comprises:

• processing the header image to identify a text item, of the text items, and the location coordinates, wherein the location coordinates include a first x value, a first y value, a second x value, and a second y value corresponding to the text item.

The method of claim 41, wherein executing the text extraction model comprises:

• identifying a text box around a text item from a first x value, a first y value, a second x value, and a second y value corresponding to the text item.

The method of claim 41, wherein executing the header segmentation model comprises:

• processing the header image to identify a bounding box, of the set of bounding boxes, wherein the bounding box corresponds to a header item.

The method of claim 41, wherein executing the box verification model comprises:

• verifying a text box corresponding to the location coordinates for a text item, of the text items, is within a single bounding box of the set of bounding boxes.

The method of claim 41, wherein executing the box verification model comprises:

• setting a verification score to a first value when each text item is within one of the set of bounding boxes.

The method of claim 41, wherein executing the box verification model comprises:

• setting the verification score to a second value when a text item is within none or multiple bounding boxes of the set of bounding boxes.

The method of claim 41, wherein generating the header retraining score comprises:

• combining a set of verification scores, comprising the verification score, for a data set to generate the header retraining score.

The method of claim 41, wherein retraining the header segmentation model comprises:

• retraining the header segmentation model when the header retraining score satisfies a header retraining threshold, wherein a raster retraining threshold is 0.9 and the header segmentation model is retrained when the header retraining score is below the header retraining threshold.

The system of claim 51, further comprising:

• combining an image with a header mask to generate the header image.

The system of claim 51, wherein executing the text extraction model comprises:

• processing the header image to identify a text item, of the text items, and the location coordinates, wherein the location coordinates include a first x value, a first y value, a second x value, and a second y value corresponding to the text item.

The system of claim 51, wherein executing the text extraction model comprises:

• identifying a text box around a text item from a first x value, a first y value, a second x value, and a second y value corresponding to the text item.

The system of claim 51, wherein executing the header segmentation model comprises:

• processing the header image to identify a bounding box, of the set of bounding boxes, wherein the bounding box corresponds to a header item.

The system of claim 51, wherein executing the box verification model comprises:

• verifying a text box corresponding to the location coordinates for a text item, of the text items, is within a single bounding box of the set of bounding boxes.

The system of claim 51, wherein executing the box verification model comprises:

• setting a verification score to a first value when each text item is within one of the set of bounding boxes.

The system of claim 51, wherein executing the box verification model comprises:

• setting the verification score to a second value when a text item is within none or multiple bounding boxes of the set of bounding boxes.

The system of claim 51, wherein generating the header retraining score comprises:

• combining a set of verification scores, comprising the verification score, for a data set to generate the header retraining score.