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Square 9’s most recent offering in the AI extraction space, dubbed FTE, involves AI assisted extraction models that are largely application/document/form independent. FTE works off of two core constructs: Forms and Tables.
Forms
Forms in FTE can be defined as FTE differs from other AI driven extraction offerings from Square 9 like TransformAI. Most notably, it’s not document specific. The tooling can operate on any document type. Like TAI however, successful extraction does have some rules for success. For TAI, those rules revolve around document characteristics that are common among Invoices and Receipts. For FTE, the rules revolve around data points being grouped into either Key / Value pairs and Tables.
Key / Value Pairs
Keys and their associated values are a core construct of extraction with FTE. For the more technical audience, Key / Value pairs are a common construct idea often used in programing and scripts. In the context of a document however, Key / Value pairs can take on new meaning.
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In either case, extracting one or both of these data points is likely better served with an alternate method. Certainly a traditional zone extraction could be perform to collect these data points. Alternately, other use case specific tools like Transform AI (which is tuned specifically for invoices and receipts) looks very specifically for data points that might match phone numbers and website addresses regardless of the presence of a Key.
Despite any limitations presented through the lack of a Key, FTE offer’s a very powerful, very accurate approach to semi-structured and unstructured document extraction that fits well into a large set of extraction use cases.
Tables
In addition to Key / Value extraction, FTE is also very good at identifying and extracting tables. Leveraging the power of AI, FTE can identify formatted tables on a document page. Because it is not bound to a document type, FTE is significantly less rigid about the tables and their associated values when compared to a feature like TAI.
Continuing with the W.B. Mason example, tables are most commonly identified by rows and columns on a page:
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While the human eye might be able to quickly determine that the table marked (2) is the table of interest for this specific document, the computer can not make such assumptions. In this image snippet, two discrete tables could be identified. Tables are most commonly and most successfully extracted when there are clearly defined rows and columns.
When FTE executes on a document page, it will return the data organized by the table it was identified in. In the case above, we would expect two tables, one with 2 columns and 8 rows (table 1) and one with 2 rows and 6 columns (table 2).