OCR Space and Google Cloud BigQuery (REST) Integration

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Extract data from documents using OCR Space and load it into Google Cloud BigQuery (REST) for analysis. Latenode's visual editor and affordable execution pricing make it easier to automate this end-to-end data pipeline, while advanced JS scripting tackles any custom logic.

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OCR Space

Google Cloud BigQuery (REST)

Step 1: Choose a Trigger

Step 2: Choose an Action

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How to connect OCR Space and Google Cloud BigQuery (REST)

Create a New Scenario to Connect OCR Space and Google Cloud BigQuery (REST)

In the workspace, click the “Create New Scenario” button.

Add the First Step

Add the first node – a trigger that will initiate the scenario when it receives the required event. Triggers can be scheduled, called by a OCR Space, triggered by another scenario, or executed manually (for testing purposes). In most cases, OCR Space or Google Cloud BigQuery (REST) will be your first step. To do this, click "Choose an app," find OCR Space or Google Cloud BigQuery (REST), and select the appropriate trigger to start the scenario.

Add the OCR Space Node

Select the OCR Space node from the app selection panel on the right.

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OCR Space

Configure the OCR Space

Click on the OCR Space node to configure it. You can modify the OCR Space URL and choose between DEV and PROD versions. You can also copy it for use in further automations.

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Add the Google Cloud BigQuery (REST) Node

Next, click the plus (+) icon on the OCR Space node, select Google Cloud BigQuery (REST) from the list of available apps, and choose the action you need from the list of nodes within Google Cloud BigQuery (REST).

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Authenticate Google Cloud BigQuery (REST)

Now, click the Google Cloud BigQuery (REST) node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your Google Cloud BigQuery (REST) settings. Authentication allows you to use Google Cloud BigQuery (REST) through Latenode.

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Configure the OCR Space and Google Cloud BigQuery (REST) Nodes

Next, configure the nodes by filling in the required parameters according to your logic. Fields marked with a red asterisk (*) are mandatory.

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Set Up the OCR Space and Google Cloud BigQuery (REST) Integration

Use various Latenode nodes to transform data and enhance your integration:

  • Branching: Create multiple branches within the scenario to handle complex logic.
  • Merging: Combine different node branches into one, passing data through it.
  • Plug n Play Nodes: Use nodes that don’t require account credentials.
  • Ask AI: Use the GPT-powered option to add AI capabilities to any node.
  • Wait: Set waiting times, either for intervals or until specific dates.
  • Sub-scenarios (Nodules): Create sub-scenarios that are encapsulated in a single node.
  • Iteration: Process arrays of data when needed.
  • Code: Write custom code or ask our AI assistant to do it for you.
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Save and Activate the Scenario

After configuring OCR Space, Google Cloud BigQuery (REST), and any additional nodes, don’t forget to save the scenario and click "Deploy." Activating the scenario ensures it will run automatically whenever the trigger node receives input or a condition is met. By default, all newly created scenarios are deactivated.

Test the Scenario

Run the scenario by clicking “Run once” and triggering an event to check if the OCR Space and Google Cloud BigQuery (REST) integration works as expected. Depending on your setup, data should flow between OCR Space and Google Cloud BigQuery (REST) (or vice versa). Easily troubleshoot the scenario by reviewing the execution history to identify and fix any issues.

Most powerful ways to connect OCR Space and Google Cloud BigQuery (REST)

OCR Space + Google Cloud BigQuery (REST) + Google Drive: When a new image is processed by OCR Space, the extracted text is stored in a BigQuery dataset. Simultaneously, the original image file is saved to a specified folder in Google Drive for archiving purposes.

Google Cloud BigQuery (REST) + OCR Space + Slack: A new row in a BigQuery table (likely populated by OCR data) triggers a query to analyze the data. If specific text patterns or anomalies are detected via the query results, a notification is sent to a designated Slack channel.

OCR Space and Google Cloud BigQuery (REST) integration alternatives

About OCR Space

Need to extract text from images or PDFs? Use OCR Space in Latenode to automatically process documents and integrate the data into your workflows. Automate invoice processing, data entry, or compliance checks. Latenode adds flexible logic, file parsing, and destinations to your OCR results, scaling beyond single-document processing.

About Google Cloud BigQuery (REST)

Automate BigQuery data workflows in Latenode. Query and analyze massive datasets directly within your automation scenarios, bypassing manual SQL. Schedule queries, transform results with JavaScript, and pipe data to other apps. Scale your data processing without complex coding or expensive per-operation fees. Perfect for reporting, analytics, and data warehousing automation.

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FAQ OCR Space and Google Cloud BigQuery (REST)

How can I connect my OCR Space account to Google Cloud BigQuery (REST) using Latenode?

To connect your OCR Space account to Google Cloud BigQuery (REST) on Latenode, follow these steps:

  • Sign in to your Latenode account.
  • Navigate to the integrations section.
  • Select OCR Space and click on "Connect".
  • Authenticate your OCR Space and Google Cloud BigQuery (REST) accounts by providing the necessary permissions.
  • Once connected, you can create workflows using both apps.

Can I analyze scanned invoice data using OCR Space and Google Cloud BigQuery (REST)?

Yes, you can. Latenode allows automated data extraction from invoices using OCR Space, then securely store and analyze this data in BigQuery, enhancing your accounting insights.

What types of tasks can I perform by integrating OCR Space with Google Cloud BigQuery (REST)?

Integrating OCR Space with Google Cloud BigQuery (REST) allows you to perform various tasks, including:

  • Automatically extract text from scanned documents and store it in BigQuery.
  • Process receipts to track expenses directly into a BigQuery data warehouse.
  • Analyze scanned forms for trends and insights, visualizing the data.
  • Create a searchable database of digitized documents in BigQuery.
  • Automate compliance reporting using data extracted from scanned records.

Howaccurate is OCR Space's data extraction with Latenode?

OCR Space's accuracy depends on image quality. Latenode allows you to preprocess images using AI, improving extraction rates.

Are there any limitations to the OCR Space and Google Cloud BigQuery (REST) integration on Latenode?

While the integration is powerful, there are certain limitations to be aware of:

  • Large volumes of image processing can consume significant API request limits.
  • Complex document layouts may require fine-tuning for optimal data extraction.
  • BigQuery storage costs can accrue based on the volume of processed data.

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