Extract Image License Plate Number Using AI Workflow Automation

Manually extracting a license plate number from images is time-consuming and prone to errors. This use case walks you through a simple n8n workflow that automates this task using an advanced AI model. This Image License Plate workflow leverages an AI to analyze an uploaded image and extract the necessary information without manual intervention.

You’ll learn how this workflow works, the key components involved, and how it automates the process from a user submission to displaying the result. This kind of Image License Plate automation reduces manual effort and increases efficiency.

What This AI Workflow Does

This workflow automates the process of extracting a license plate number from an image.

Starts with a Form Submission: A user uploads an image (JPG or PNG) via a form labeled “Analyse image”.

Set Up the Task: A settings node defines the prompt for the task. This tells the AI to extract the license plate number from the front-most car in the uploaded image and return only the characters, with nothing extra.

Processes the Image: The image is sent as binary data to an LLM chain that uses an AI model (configured via OpenRouter and GPT-4o). This model analyzes the image according to the defined prompt.

Displays the Result: The extracted license plate number (or output text) is then shown to the user on a results page labeled “Extracted information”.

Step-by-Step Breakdown

This section details the individual nodes that make up the workflow for this Image License Plate extraction process.

FromTrigger: This is the starting point of the workflow.

It presents a form titled “Analyse image”.
The form has a file upload field named “Image” that accepts .jpg and .png file types.
This is where a user uploads the image.

Settings: This node defines the parameters for the AI task.

It sets the model to openai/gpt-4o.
It defines the prompt to “Extract the number of the license plate on the front-most car depicted in the attached image and return only the extracted characters without any other text or structure”.

Basic LLM Chain: This node is the core of the AI processing.

It takes the prompt from the Settings node.
It receives the uploaded image as binary data.
It uses the connected LLM to analyze the image based on the prompt.

OpenRouter LLM: This node provides the AI model itself.

It is configured to use the openai/gpt-4o model.
It connects to the Basic LLM Chain node.

FormResultPage: This node displays the final output to the user.

It has a completion title of “Extracted information:”.
It displays the text output from the Basic LLM Chain node, which is the extracted license plate number.

This workflow shows how you can easily create an Image License Plate extractor.

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Key Features of This AI Workflow Automation

No-code setup: The workflow is configured using pre-built nodes in n8n.
Direct image processing: The AI model processes the image directly.
Customizable prompts: The prompt can be easily changed to extract different information.
Automated display: The result is automatically shown to the user without manual steps.
Quick results: The entire process from upload to display takes very little time.

This powerful Image License Plate automation is a great example of how AI can handle specific, repetitive tasks.

Business Use Cases for Image License Plate Extraction

Parking Management: Automate entry and exit for registered vehicles.
Fleet Tracking: Monitor and log vehicle movements.
Toll Collection: Automatically bill vehicles based on their plate number.
Security: Use the plate number for access control.

This type of Image License Plate extraction can be implemented in various scenarios to improve efficiency and reduce manual labor.

Relevant Reads:

Conclusion

This n8n workflow provides a robust solution for extracting a license plate number from an image uploaded via a form. It demonstrates the power of AI to handle specific data extraction tasks from visual data. By combining a simple user interface with a powerful AI model, this workflow delivers an efficient and accurate Image License Plate solution.

FAQs

What formats does this workflow support for image uploads?

The workflow is configured to accept images in .jpg and .png formats.

Can I use a different AI model for license plate extraction?

Yes, the workflow uses an OpenRouter LLM node, which can be configured to use various models. You could switch it to another model that supports vision capabilities.

Does this workflow only extract license plates?

The workflow’s function is determined by the prompt set in the Settings node. By changing the prompt, you could configure the AI to extract different information from an image, demonstrating the flexibility of this image licensing plate automation.

#Extract Image