2.80:Infer Grid (Table Extract Method)
Infer Grid is one of three methods to extract data from tables on documents. It uses the positional location of row and column headers to interpret where a tabluar grid would be around each value in a table and extract values from each cell in the interpreted grid.
This method extracts information by inferring a grid from the header positions. This is done by assigning an "X Axis Extractor" to match the column headers and a "Y Axis Extractor" to match row headers. A grid is created from the header positions extracted from the two extractors. Furthermore, if table line positions can be obtained from a Line Detection or Line Removal IP Command, only one Axis Extractor is needed. In these cases, the X Axis Extractor can be used to find the column header labels, and the grid will be created using the table lines in the documents layout data. The raw text data obtained from the Recognize activity will populate each cell of the grid according to where it is on the page.
Use Cases
Non-Standard Tables
The Infer Grid method excels at many cases where the table structure is not easily understood by the Row Match or Header-Value methods. This is especially true for tables with table lines present. Examine the table below.
Row Match might work, but it would be a heavy lift. First, each row's pattern is different. There are names on one, addresses on another, phone numbers on another. Every row has a different pattern. It would take some creative configuration. You could try to make a row out of the columns. It would take a series of extractors, be very effort intensive and complicated to set up.
Header-Value would also have problems. The column header labels ("Lender", "Mortgage Broker", etc), would be straightforward. But the value extractors would be tricky. It's possible a generic text segment extractor could get you close, but at least the "Address" row presents problems because it is a two line value instead of a single line. Again, it could be doable, but it would take some effort.
Row Match can do this job with a single extractor. All you would need to do is write an extractor to find the "Y Axis"; so all the column header labels in a row.
Since table lines are present, the text falling inside each cell (obtained via the Recognize activity could be extracted to the corresponding cell in the column
Furthermore, if table lines are not present, Infer Grid can use both both the row and column header labels by using both the "Y Axis Extractor" and "X Axis Extractor" properties. We can use two extractors, one to return all the Y Axis labels and one to return the X Axis labels, and use their positions to infer the table's structure.
OMR Checkboxes
The Infer Grid method is the easiest way to read checkbox states inside a table. Once the table's structure is found using the axis extractors, you can choose which columns contain checkboxes. Grooper will use layout data obtained from a Box Detection or Box Removal IP Command to determine if the box is filled in or left blank. Refer to the tutorial below for more information on how to configure this use.
Re-OCRing Tricky Cells
The Infer Grid method also allows you to choose a column and apply a secondary OCR profile to the cells within that column. This is useful for tables that have specialized fonts for values filled inside the cells.
For example, the OCR-A font is not easily read by most modern OCR engines. However, Google's Tesseract OCR engine has some specialized functionality for the font. A document using a column like the one to the left could process most of the document, using an OCR profile that reads conventional fonts, including the column headers such as "Date". Then, the cells inside the grid, containing dates in the OCR-A font, could be reprocessed using another OCR profile that uses the Tesseract engine.
How To
Creating a Data Table in Grooper
Before you begin
A Data Table is a Data Element used to model and extract a table's information on a document. Just like other Data Elements, such as Data Fields and Data Sections, Data Tables are created as children of a Data Model. This guide assumes you have created a Content Model with a Data Model.
We will use the table below as our example for creating a Data Table.
Using the Node Tree on the left side of Grooper Design Studio, navigate to the Data Model you wish to add the Data Table to. Data Tables can be created as children of any Data Model at any hierarchy in a Content Model.
Add a Data Table
Right click the Data Model object, mouse over "Add" and select "Data Table"
The following window will appear. Name the table whatever you would like and press "OK" when finished.
This creates a new Data Table object in the Node Tree underneath the Data Model.
Add Data Columns
Right click the Data Table object, mouse over "Add" and select "Data Column"
This brings up the following window to name the Data Column. When finished, press "OK" to create the object.
This creates a new Data Column object in the Node Tree underneath the Data Model.
Repeat Until Finished
Add as many columns as necessary to complete the table. For our example, we have a single Data Table with five Data Columns, each one named for the corresponding column on the document.
Configure Infer Grid for OMR Checkboxes
| ! | Some of the tabs in this tutorial are longer than the others. Please scroll to the bottom of each step's tab before going to the step. |
A Data Table is a Data Element used to model and extract a table's information on a document. Just like other Data Elements, such as Data Fields and Data Sections, Data Tables are created as children of a Data Model. This guide assumes you have created a Content Model with a Data Model.
We will use the table below as our example. This is a mockup of a government form using OMR checkboxes to check off whether or not certain critera listed in the "Description" column is met.
Add a Data Table
Create a Data Table with three Data Columns. The five columns for our example are "Operator Name", "Well Name", "Lease Number", "PC", and "Runs". Refer to the Creating a Data Table section above for more information on adding a Data Table to a Data Model.
Set the Extract Method
First, set the "Extract Method" property to "Infer Grid". (1) Select the Data Table object in the Node Tree (2) Select the "Extract Method" property. (3) Using the dropdown list, select "Infer Grid"
Configure the Axis Extractor
The first step when configuring Infer Grid for any table is to configure the Axis Extractors. These are extractors written to locate the column and row header label locations. Once these locations are known, infer grid can interpret a grid structure where it expects to find each cell in the table.
For our document, our table uses lines to divide its rows and columns into bounded cells. Because of this, we can get away with only using a single axis. We will use the "Y Axis Extractor" property to locate the headers "Description", "Farm", and "Simulator".
These headers can be found with a simple Internal pattern (Although if your documents are more complicated, you can use a Reference to an extractor created in the Node Tree). Expand the "X Axis Extractor" property. Select the "Type" property and choose "Internal" from the dropdown list.
Select the "Pattern" property and press the ellipsis button at the end to bring up the Pattern Editor.
We will write a single pattern to match each column header. The following pattern will work just fine.
description\t farm\t simulator
| ! | This pattern uses tabs as anchors between column header label. Don't forget to turn on "Tab Marking" in the "Properties" tab. It is found by expanding the "Preprocessing Options" property. |
We've returned all our header labels smashed together as a single result. But Infer Grid needs individual instances of each header. Just like we did in the #Using Row Match with Named Groups|Using Row Match with Name Groups tutorial, we will use named groups to create instances. This time, we will create instances for each column label, from which Infer Grid will use their positions on the document to create a grid.
Select each label (without the tab character) in the Value Editor and make a named group out of each one. You can either right click the selection and choose "Create Group" option or use the Ctrl + G hotkey on your keyboard.
| ! | Remember to name the groups the same as their corresponding Data Column. That way the instances results will populate the correct Data Column in the Data Table. |
That will make the full regex the pattern below.
(?<Description>description)\t (?<Farm>farm)\t (?<Simulator>simulator)