This n8n workflow automates the process of extracting license plate numbers from uploaded images, using AI models for image analysis and language processing. The workflow begins with a user uploading a vehicle image via a web form. This image is then processed through an AI model (OpenRouter) to analyze the content. The extracted image data is sent to a language model (GPT-4) to specifically identify and extract the license plate number, omitting any additional text or structure. Finally, the result is displayed on a web page for review.
Step-by-step, the workflow operates as follows:
1. It starts with a trigger node that listens for image uploads through a web form, allowing users to upload JPG or PNG images.
2. The uploaded image is passed to an OpenRouter AI model node, which analyzes the image and generates a descriptive output.
3. The output, including binary image data, is fed into a language model (GPT-4), with a specific prompt to extract only the license plate number.
4. The extracted license plate number is then displayed to the user on a dedicated result page.
This process is useful in parking management, toll collection, vehicle inspection, or any scenario requiring quick and automated license plate recognition from images uploaded through a web interface. It combines user input, AI-based image analysis, and natural language processing to deliver accurate, fast results without manual intervention.
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