An ML model that detects and recognizes vehicle registration plate numbers
The Number plate recognizer is a machine-learning model that detects and recognizes vehicle registration plate numbers in images.
It is developed and trained to detect vehicle license plates, recognize the characters on the plate, and return them as text strings. The model takes in an input image containing a vehicle and outputs the alphanumeric number present on the vehicle's registration plate in text form.
The process of license plate recognition consists of the following steps:
License plate detection;
Pre-processing the resulting image from Step 1 (warping, deskewing) to prepare it for optical character recognition (OCR);
Passing the image from Step 2 through an OCR engine and receiving the recognized characters.
Some typical use cases for this model include:
Efficient vehicle cataloging - Vehicles can automatically be cataloged in a database. When new vehicle images are uploaded, the model can extract the registration plate numbers and associate them with the corresponding vehicle records, allowing for quick and accurate identification and retrieval of vehicle assets.
Smart searching - Users can leverage plate number recognition to perform targeted searches and easily locate specific vehicles, thus saving time and effort.
Automated metadata - Vehicle assets can be tagged with relevant metadata based on their registration plate numbers. This metadata may include vehicle make, model, year, and other information associated with the recognized plate.
Regulatory compliance - The model can aid in ensuring compliance with legal requirements related to vehicle registration and documentation.
An up-to-date reference with all API endpoints is available here:
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