Search: extract-structured-data

Each Pipeline defines the structure of Data fields that akaBot Vision extracts. Description When editing this structure you have two options: Use pre-trained Data fields – AkaBot Vision’s Generic AI engine has been pre-trained to recognize specific Data fields and enables you to start extracting data

[3] Configure Fields for Data Extraction

Customizing Data Extract

Configuring Fields for Data Extraction

="wikigeneratedid" id="HParagraph1" %) Each Pipeline defines the structure of Data fields that akaBot Vision extracts. == **Description** == (% class="wikigeneratedid" %) When editing this structure you have two

to recognize specific Data fields and enables you to start extracting data without any additional training

1. Create an Account Note: Although akaBot Vision currently supports Pre-trained data fields only for Invoice processing, the technology is documented agnostic and can extract data from any structured document including receipts, purchase orders, shipping documents, etc. Please contact support

currently supports Pre-trained data fields only for Invoice processing, the technology is documented agnostic and can extract data from any structured document including receipts, purchase orders, shipping

-20220420182302-1.png||alt="image-20220420183141-4.png" data-xwiki-image-style-alignment="center"]] **Step 2

A basic element in the extraction schema is the data field. However, akaBot Vision enables the capture of even more complex structures like tables. Adding a predefined table field If you are missing

settings. In this tab, you can manage pre-trained data fields and select which of them should be extracted

[4] Capture Custom Table Data

Customizing Data Extract

Capturing Custom Table Data in akaBot Vision

="wikigeneratedid" id="HParagraph1" %) A basic element in the extraction schema is the data field. However, akaBot Vision enables the capture of even more complex structures like tables. == **Adding a predefined table

of them should be extracted. (% style="text-align:center" %) [[image:image-20220421003652-1.png||data

Automation of Fields Capture Custom Table Data Configure Automation Type for Pipeline Configure Fields for Data Extraction

[6] Customize Data Extract

Customizing Data Extract

/Configure%20Automation%20Type%20for%20Pipeline%20/]] * [[Configure Fields for Data Extraction>>path:/bin

* [[Automation of Fields>>path:/bin/view/akaBot%20Vision/Customizing%20Data%20Extract/Automation%20of%20Fields%20in%20akaBot%20Vision/]] * [[Capture Custom Table Data>>path:/bin/view/akaBot%20Vision

-card-title">Customize Data Extract</div> <div class="akb-card-body"> <div class="akb-link

%20for%20Data%20Extraction/">Configure Fields for Data Extraction</a> <a href="/bin/view/akaBot

</a> <a href="/bin/view/akaBot%20Vision/Quick%20start%20guide/Validate%20the%20Data/">Validate the Data

When you select a document for review, it will take you to the Validation screen. In the left panel, you can see the predefined list of Data fields that have been extracted. Press Tab to check

be able to export the data. If you realize that the data field was not extracted correctly, you can make

[3] Validate the Data

Validate the Data

, otherwise, you won’t be able to export the data. If you realize that the data field was not extracted

="font-family:Arial,Helvetica,sans-serif" %)In the left panel, you can see the predefined list of Data fields that have been extracted. (% style="font-family:Arial,Helvetica,sans-serif" %)[[[[image:image

extracting the data fields. The AI extraction typically takes up to 5 seconds for 1 A4 page. After

]] [[image:image-20220420184331-2.png||data-xwiki-image-style-alignment="center"]] Click “OK” and the AI will automatically begin extracting the data fields. The AI extraction typically takes up to 5 seconds for 1 A4 page. [[image:image-20220420184331-3.png||data-xwiki-image-style-alignment="center"]] After processing

documents to extract data. Staff can create a new learning model by following these below steps: Step 1

in documents. This helps the model will extract data more exactly. Staff can choose "Label" or "Value

. After training models successfully, staff can use that model to extract data by creating new pipeline

, send the learning models to production and use them to run on actual documents to extract data. Staff

" for each field if having enough data in documents. This helps the model will extract data more exactly

successfully, staff can use that model to extract data by creating new pipeline with document type is the model