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            Extract Tables for Intelligent Form Reader

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            Extract Tables for Intelligent Form Reader

            Enable Amazon Textract Tables to extract tabular information from the documents.

            Required Editions

            Available in: Lightning Experience
            Available in: Financial Services Cloud, Health Cloud, and Public Sector Solutions
            Intelligent Form Reader is available for an additional cost with the Intelligent Form Reader add-on license.
            User Permissions Needed
            To edit Intelligent Form Reader settings: Customize Application
            Note
            Note The Maximum Pages and Confidence Score Threshold fields in the global content extraction settings are available only in Health Cloud.
            1. Enable Amazon Textract Tables.
              1. From Setup, in the Quick Find box, enter Intelligent Form Reader, and then select Intelligent Form Reader.
              2. In the Global Content Extraction Settings section, click Edit Settings.
              3. Enter the maximum number of consecutive pages from the first page of a document that you want to send for content extraction at a time.
                The default value for Maximum Pages is 5. If a user selects pages that are already scanned, they won’t count against the limit.
              4. Enable Amazon Textract Tables to extract information from tables in the document.
                Once enabled, Amazon Textract Tables extracts information from tables in addition to other enabled Textract APIs (such as Forms and Queries).
            2. Create a template that has table information.
              1. From Setup, in the Quick Find box, enter Intelligent Form Reader, and then select Intelligent Form Reader.
              2. Click New Template.
              3. Enter a name and an API name for the template.
                The API name is automatically generated based on the template name and can be customized. Ensure that each template has a unique API name.
              4. Select the document type that you want to associate with the document.
                To extract information from identity documents, select a document type that has the Identity Document form type.
              5. Click Upload File.
              6. Click Continue.
                Enabling Amazon Textract Tables creates separate tabs for forms and tables, helping you analyze different document structures effectively. Each extracted table gets a sequential label (Table 1, Table 2, and so forth) for easy identification.
              7. Select a target object from the Target Object drop-down.
                Tip
                Tip
                • You can map document fields to fields of multiple objects by changing the target object.
                • The list of objects visible to users depends on their access level. Ensure users have appropriate permissions to view the required objects.
              8. Optionally, select a record type of the target object from the Object Record Type drop-down.
                The Default record type is selected by default. The Default record type corresponds to the Master record type that's used when a record has no custom record type associated with it.
              9. Select a target object field or a target object’s record type field in the Target Object Fields column for the required document fields.
                As the table data is mapped at the column level, you need to establish a connection between each extracted table column and a corresponding field in the target object.
                Tip
                Tip
                • Use Amazon Textract queries when incoming documents contain duplicate or conflicting fields. By using Amazon Textract queries, you can create different aliases and queries for each set of data and decide where to place them. For example, if a document contains the phone numbers of your users and the service provider, you can create two separate aliases and queries and then use the aliases to map the required fields.
                • During document transformation, Amazon Textract queries use the table and page numbers specified in the template to identify specific tables (along with rows within them), ensuring accurate mapping of extracted data to the corresponding fields.
              10. Add all the missing document fields and map all the required document fields to the target object fields or the target object record type fields,
              11. Save your changes, and then click Activate to activate the template.
              You can also map a few document fields and save the mapped fields with unmapped document fields.
            3. Transform documents that have tables.
              1. Access the Transform Document page, which includes Forms and Tables tabs.
              2. On the Forms tab, select the pages containing the required data. You can either select all pages or select Custom to specify particular pages.
              3. On the Tables tab, you can select one page at a time.
                You will receive a confirmation prompt on clicking the Tables tab, as leaving the page removes the data you've entered in the Forms tab.
              4. To exclude rows that you do not need to save, select Skip record.
              5. Select the Object to save the table information. Ensure that the object is the same as the selection made while creating the template.
              6. Review the document and the mapped information, navigating between records as needed.
              7. To move to the next record, click Continue.
                The user gets an error message if any mandatory fields are left blank. Ensure to resolve any mandatory field errors before proceeding.
              8. Save your changes after resolving all the errors. Each table row creates a record in the selected object. Skipping a row in the table is not currently possible.
                Document Checklist Items are automatically generated for each record. Reviewing any Document Checklist item highlights the corresponding row from which the record was created.
             
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