Search: join-data-table

で、｢Form Field｣の行にある｢Add Field｣ボタンをクリックします。 ステップ2：「Label」にテーブル名を入力し、｢Data Type｣の「table」を選択します。 ステップ3

をクリックします。 ステップ2：「Label」にフィールド名を入力し、「Data Type」でフィールドのデータ型を選択します。 ステップ3：（オプション）｢Required｣ボタンをオン

||cursorshover="true"]] (% class="box infomessage" %) ((( **ステップ2**：「Label」にテーブル名を入力し、｢Data Type｣の「table」を選択

"]] (% class="box infomessage" %) ((( **ステップ2：**「Label」にフィールド名を入力し、「Data Type」でフィールドのデータ型を選択します。 ))) [[image

at: [email protected] for more assistant. Step 4: Choose one or more users to join this pipeline for reviewing documents (if need). Step 5: Click "OK" button to complete creating a new pipeline. Table

:1704877579310-775.png||height="377" width="566"]] **Step 4**: Choose one or more users to join this pipeline

" %) ((( Table of Content ))) {{toc depth="4" start="2"/}} ))) )))

and choose data type is "Table". Step 3: (This step is optional) Turn on button "Required" to set require

and data type for each column Step 6: Click "Save" button Table of Content

and Table Field 1. Add Form Field Step 1: On Add Learning Instance screen, click "Add Field" button

the table name on "Label" field and choose data type is "Table". ))) [[image:image-20221028164141-11.png

types of fields: Form Field and Table Field == **1. Add Form Field** == (% class="box infomessage

the field name on "Label" field and choose data type for field on "Data Type" field ))) [[image:image

After importing the document successfully and the data extraction process is successfully finished

, akaBot Vision provides users with the capability to add or remove rows in a table To insert a row, you can click "+" icon To delete a row, you can click "x" icon To add a new row at the end of the table

="wikigeneratedid" %) After importing the document successfully and the data extraction process is successfully

with this status in the “To review” tab in the user interface. [[image:image-20220420193327-1.png||data-xwiki

in each field that has been detected incorrectly [[image:image-20220420193327-2.png||data-xwiki-image

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

choose base model, staff will have to create form fields and table from scratch Step 5: Click "Save

. With the Form Fields, staff should label both "Label" and "Value" for each field if having enough data

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

||cursorshover="true"]] * If staff doesn't choose base model, staff will have to create form fields and table

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

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

customizable, so you can add/group/remove pipelines as needed. Table of Content

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

: Choose Automation Type and set conditions for required fields and data formats The Automation Type

will be bypassed Bypass wrong data formats: All the documents with wrong data formats inside will be moved

. If you turn this mode on, all the wrong data formats will be bypassed Step 3: Click [Save] to save

Customizing Data Extract

Automation Type and set conditions for required fields and data formats ))) * The Automation Type will have

fields will be bypassed * Bypass wrong data formats: All the documents with wrong data formats inside

these later. If you turn this mode on, all the wrong data formats will be bypassed [[image:image

As a manager or administrator, users may want to know about, for instance, all the documents imported to a specific queue or which data fields required the most corrections. The dashboard’s reports

by a selected granularity. Table Of Content

to a specific queue or which data fields required the most corrections. The dashboard’s reports could also help

-663.png]] ))) (% class="akb-toc" %) ((( (% class="akb-toc-title" %) ((( Table Of Content

).pdf", "pipelineName": "not split never auth", "data": [ { "fieldName": "Customer Name

}, { "fieldName": "Table Description", "fieldValue": null, "tableValue": [ ] }, { "fieldName": "Table Quantity", "fieldValue": null, "tableValue

organization's data and account information. In this document, you will find an introduction to the API usage

is processed successfully and the user can export data via calling API Export Document with input parameters

to review documents then change it to "Confirmed" status by API Update Document Status before exporting data

** == The akaBot Vision API allows you to programmatically access and manage your organization's data and account

and the user can export data via calling API (%%)[[Export Document>>https://docs.akabot.com/bin/view/akaBot

%20with%20RPA/API%20Automation/#H4.UpdateDocumentStatus]](% style="color:#000000" %) before exporting data