Search: insert-data-table

. Adding a new table In order to create a new table, click [Add Field], select Data Type = “table

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

[4] Capture Custom Table Data

Capturing Custom Table Data in akaBot Vision

Customizing Data Extract

, click [Add Field], select Data Type = “table” and then input the value you want to create a new table

="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

抽出スキーマの基本要素はデータフィールドです。ただし、akaBot Visionを使用すると、テーブルなどのさらに複雑なデータ構造をキャプチャできます。 事前定義されたテーブルフィールドの追加 ドキュメントからデータをキャプチャするときに一部のフィールドが欠落している場合は、キュー設定の｢Fields to capture｣タブに移動します。このタブでは、事前トレーニングされたデータフィールドを管理し、どのフィールドを抽出するかを選択できます。 新しいテーブルの追加 新しいテーブルを作成するには、｢Add Field｣をクリックし、｢Data Type｣の｢table｣を選択して、新しい

-20220421003652-1.png||data-xwiki-image-style-alignment="center"]] == **新しいテーブルの追加** == 新しいテーブルを作成するには、｢Add Field｣をクリックし、｢Data Type｣の｢table｣を選択して、新しいテーブルの値を入力します。 (% style="text-align:center" %) [[image:image-20220421003652-2.png||data-xwiki-image-style-alignment="center"]] ))) ))) ))) (% class="akb-toc

Capturing Custom Table Data in akaBot Vision

Customizing Data Extract

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

, 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

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

or remove rows in a table * To insert a row, you can click "+" icon * To delete a row, you can click "x

="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

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

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

[6] Customize Data Extract

Customizing Data Extract

* [[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

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

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

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

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 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

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