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This topic covers how to create an AI skill to extract text from documents using machine learning (ML) models.
An AI skill is a design object that enables you to build, configure, and train an ML model using low-code. A Document Extraction skill takes a document as input via the Extract from Document smart service, analyzes the document, and returns a map of the text extracted from the document.
You'll create a document extraction AI skill for each type of document you want to extract data from.
Here's a high-level breakdown of how to create and use a document extraction AI skill:
In the Build view, click NEW > AI Skill.
Select the skill type you want to create.
Configure the following properties:
|Name||Enter a name that follows the recommended naming standard.|
|Description||(Optional) Enter a brief description of the AI skill.|
Next, you can define the document structure.
The document extraction AI skill is powered by a machine learning model designed to extract data from structured and semi-structured forms. The skill can be useful for documents businesses commonly receive, such as invoices or purchase orders.
To get started, you'll add the fields that commonly appear in the document type. For example, if you're building an AI skill to extract data from invoices, you'll want to add common fields that contain the data you want to extract, such as Invoice Number, Date, and Vendor Name.
Before Appian can extract data from your documents, you'll need to define what those fields are and the type of data they contain:
Add a field for each piece of data in the document you want to extract using the AI skill. If you don't add a field, the skill won't know to extract the data or how to label it.
The document extraction AI skill extracts data as type Text or Boolean (for checkboxes). However, your document data may be of different data types, such as Dates or Numbers. Additionally, you may want to save this extracted data in specific data objects, such as record types or custom data types (CDTs).
To ensure your data reaches the intended destination and in the proper format, you'll need to properly name and map fields as you configure your AI skill and smart service.
Pick the right name for the field as you define your document structure in the AI skill.
Flat data: If you're planning to save extracted data to a flat CDT or record type, name each field in the document structure to match the target field so Appian casts it properly.
invoiceNumberfield in your
InvoicesReceivablerecord type. To achieve this, create a
invoiceNumberfield (type: Text) in your skill to match its ultimate destination (the
invoiceNumberfield in your record type) in Appian.
Tables (nested data): If you plan to extract document data that's formatted in a table, you'll need to carefully name these fields as well. When Appian extracts data from a table in a document, the results is a list of maps, with each map representing a row in the table. The name you choose for the table field helps Appian cast all of this data properly.
Name the table fields based on the data object where you plan to store it:
Example: You want to extract data from invoices. Each invoice has a table containing a list of items you ordered, the unit costs, and how many of each item you ordered. Invoice data is stored in the
Invoice CDT, which contains a field
Invoice_Table CDT) to store itemized data from tables in each invoice. You want to configure your process so that the smart service saves the itemized values into the
To achieve this, create a
lineItems (type: Table) field in your skill to match its ultimate destination (the
Invoice_Table CDT, by way of the
lineItems field of the
Invoice CDT) in Appian. Additionally, name each field within the table to match the corresponding fields within the CDT as well.
The following image include numbers to demonstrate the fields that should match when extracting table data with an AI skill.
The following table summarizes guidance for naming fields in your document structure:
|Extracted data type||Target location||Field name requirement||Example field name|
|Text or Boolean||Record type or CDT||Field name must match the target field.||
|Table||Record type relationship||Field name must match the name of the relationship.||
|Table||CDT||Field name must match the name of the field that contains table data. Fields within the table must match the names of the fields within the CDT.||
As you build your process, you'll map the smart service output to process variables. Appian supports casting data from Maps to CDTs or record types so you can use the extraction results to write to your database.
Review the design guidance to ensure that the fields in your AI skill are named based on how you want to cast the data.
To ensure that the AI skill extracts and uses your data as you intend, carefully map the Extract from Document smart service output to the proper variables so your data is used or stored properly:
Now you're ready to use your document extraction skill in a process.
Add the Extract from Document smart service and configure it to call your new skill.
You'll also want to add the Reconcile Doc Extraction smart service so a person can verify that the extracted data maps to the proper fields.
There are multiple ways to integrate document extraction into an Appian process. No matter the method you use, you may be curious how it works. This section provides more detail on how Appian extracts and maps data from your documents.
First, it's important to remember that Appian document extraction is powered by pre-trained machine learning (ML) models, allowing you to get started quickly. When you extract document data in Appian, you aren't creating a model or training one on data you provide. Instead, Appian learns about your data via reconciliation tasks. Appian applies this learning in subsequent extractions of the same document type, but the model is not retrained.
The document extraction process – either within the Extract from Document or Start Doc Extraction smart services – consists of three parts:
See the Document Extraction glossary for a refresher on the terms used on this page.
Output: Identified text, key-value pairs, checkboxes, and tables
In the first step, the PDF goes to a pre-trained ML model to run optical character recognition (OCR), extract key-value pairs, and identify special document formatting checkboxes and tables. The model returns all identified values (represented by blue bounding boxes in the image below). Keys are represented by purple bounding boxes for reference, but they do not appear in Appian.
Input: Identified text, key-value pairs, checkboxes, and tables from step 1
Output: Reconciliation user input task
Next, Appian leverages previous mappings stored in the customer's environment to know which extracted data to map to the document structure. These mappings are stored in a dictionary as you complete reconciliation tasks over time (step 3). So, the more mappings and reconciliation tasks you complete for a given document type, the better Appian is at mapping that data. Each subsequent reconciliation task is faster and more accurate.
If your Appian environment has previously mapped values to your structured fields, Appian leverages those previous keys to assist in mapping the data before assigning a reconciliation task.
In the image above, Appian extracted
Invoice# as the key and
INV-12 as the value in this document. In the document structure, there is a field named
invoiceNumber don't match, but that's okay! If you previously mapped the
Invoice# key to the
invoiceNumber field in a reconciliation task, Appian should automatically map this data for you.
Each time a user completes a reconciliation task, Appian stores updated mappings in a simple dictionary of terms (keys and positions) to use next time it has to map data from the pre-trained model (output of step 1) to the structured fields in your application.
Input: Identified and mapped text, key-value pairs, checkboxes, and tables from step 2
Output: Auto-extracted fields to Appian process model for use in your application
Finally, a user completes a reconciliation task to confirm that the mappings from step 2 are correct. When a user maps data to a field and submits the reconciliation task, Appian stores the label for the key that was mapped. For example, if you provide mappings, Appian will recognize that
P.O. #, and
PO No. both map to the
poNumber Appian data type field.
Reconciliation helps Appian manage variations in semi-structured and structured forms. In this way, reconciliation helps document extraction learn more about your data.
As you complete reconciliation tasks, data mapping in step 2 improves because Appian can match the keys to more options. However, the model in step 1 does not get retrained when you submit a reconciliation task with updated mapping. This means that if the ML model misses a field in step 1, Appian will continue to miss that field in step 2, and that there are some forms where auto-extraction will not extract the data desired. In these situations, customers can use manual extraction in step 3 to get the last pieces of information.
Create a Document Extraction AI Skill