If this tool helped you, you can buy us a coffee ☕
Use AI to recognize license plate numbers and origins from images. Supports multiple plate types and input formats.
Enter text to start extraction
When you need to quickly extract license plate information from vehicle photos, manual recording is both time-consuming and prone to errors. The License Plate Extractor is an online tool based on AI visual recognition technology that automatically locates, segments, and recognizes license plate characters from uploaded images. It outputs structured data including the license plate number, color, confidence score, and origin. Its core processing targets are vehicle license plates complying with the Chinese motor vehicle license plate standard (GA36-2018), covering blue, yellow, green (new energy), and white plate types.
Q: What image input formats does the License Plate Extractor support?
A: It supports common web image formats like JPG and PNG. We recommend keeping the image size under 5MB.
Q: What confidence score is considered reliable?
A: A confidence score of 90% or higher is considered high-precision recognition. If it's below 80%, we recommend retaking a clearer photo of the license plate.
Please ensure the license plate area in the uploaded image is clearly visible, avoiding strong reflections, blurriness, or severe occlusion. User data is not stored during image processing, but we advise against uploading panoramic vehicle photos containing personal privacy information. For license plates with a resolution below 100px or a tilt exceeding 45 degrees, the recognition accuracy will significantly decrease.
To improve the recognition success rate, it is recommended to have the license plate occupy at least 1/4 of the image area and maintain a frontal angle when shooting. Typical input/output example: Input a photo of the front of a vehicle with the blue license plate "京A12345", and the output will be {"Number":"京A12345","Color":"Blue","Confidence":98.5%,"Origin":"Beijing"}. This tool uses an end-to-end deep learning model and is specifically optimized for the green gradient background of new energy vehicle plates.
