Image to License Plate Number
This workflow can automatically identify and extract license plate numbers from uploaded vehicle images, directly returning clean license plate characters, eliminating the need for manual input by users. By integrating advanced large language models, it significantly improves the efficiency and accuracy of license plate recognition, streamlining the traditional license plate extraction process. It is applicable in various scenarios such as traffic management, parking lots, and logistics monitoring, helping users achieve rapid automated collection of vehicle information, enhance management intelligence, and save time and labor costs.
Workflow Name
Image to License Plate Number
Key Features and Highlights
This workflow automatically recognizes and extracts the license plate number of the foremost vehicle in an uploaded image, returning clean license plate characters directly without requiring manual input or post-processing. By leveraging advanced large language models (such as OpenAI GPT-4o) combined with image input, it delivers efficient and accurate license plate recognition services.
Core Problems Addressed
Traditional license plate recognition typically relies on specialized OCR software or complex image processing algorithms, which have high deployment and usage barriers and often require manual verification of results. This workflow integrates large language models to understand image content, simplifying the license plate extraction process while improving recognition accuracy and automation.
Application Scenarios
- Automated vehicle information entry for traffic management authorities
- Automatic vehicle identification at parking lot entrances and exits
- Monitoring and recording of logistics vehicles
- Traffic violation capture and evidence collection
- Vehicle identity recognition in intelligent security systems
Main Process Steps
- Users upload vehicle images via a form (supports JPG and PNG formats).
- The trigger node receives the image data and sets license plate recognition prompts and model parameters.
- Using OpenRouter to connect with the GPT-4o model, the image is input and license plate information is parsed through the “Basic LLM Chain” node.
- The parsed result is returned as plain text and displayed to the user on the form result page.
Involved Systems or Services
- n8n Form Trigger Node (formTrigger) for receiving user-uploaded images.
- OpenRouter LLM (GPT-4o model) serving as the core AI engine for image understanding and text generation.
- Basic LLM Chain Node responsible for passing binary image data and prompts to the language model.
- n8n Form Result Node to present the final extracted license plate text.
Target Users and Value
- IT and development teams seeking to quickly build automated license plate recognition workflows without complex programming.
- Traffic management and security industry users requiring efficient and accurate license plate data collection solutions.
- Parking lot operators, logistics companies, and others aiming to enhance vehicle management intelligence.
- Any scenario needing rapid extraction of license plate numbers from vehicle images can leverage this workflow for automated recognition, saving time and labor costs.