Practical overview
When it fits
Use FastALPR when a Python application needs plate detection and text recognition from images, and you can choose and install an ONNX backend appropriate to its runtime environment.
Who it is for
- Python developers adding license-plate recognition to image-processing applications
- Computer-vision developers who need a plate-detection and OCR component
- Teams prototyping image-based vehicle-entry workflows
Documented uses
- Detect a license plate and read its text from an image.
- Return recognition results for use in a larger Python application.
- Draw predictions on an image for visual review.
- Replace the default OCR or detection model with a chosen alternative.
Requirements & limits
The repository identifies the project as MIT-licensed. Inference requires an ONNX backend installed separately through an appropriate package extra.
- FastALPR combines plate detection and OCR, but inference requires an ONNX runtime that is not installed by default.
- Available installation extras cover CPU, CUDA, OpenVINO, DirectML, and QNN; CUDA is listed for Linux and Windows, and DirectML for Windows.
- The framework supports replacing detection and OCR components; the README demonstrates a custom Tesseract OCR implementation.
- The documented usage accepts image paths and can return an annotated image; a complete camera-capture or vehicle-access system is outside the shown example.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Run recognition on an image with FastALPR
Use the documented Python example to obtain license-plate recognition results from an image.
Before you start
- Python package installed with an ONNX backend suitable for the environment
- An image file to process
Steps
Install FastALPR with an appropriate package extra, such as
fast-alpr[onnx]for CPU inference.Import
ALPRfromfast_alprand initialize it with detector and OCR model names.Pass the image path to
alpr.predictand inspect or print the returned results.
Expected outcome
FastALPR returns recognition results for the supplied image.
Guide limitations
- The package does not install an ONNX runtime by default; an ONNX backend is required for inference.
- The example processes a still image and does not document camera capture or a complete vehicle-entry workflow.
Use Tesseract as the OCR component
Adapt the documented OCR interface to use Tesseract while retaining FastALPR's detection flow.
Before you start
- FastALPR and an ONNX backend for inference
- NumPy and pytesseract, as used in the documented example
- A plate-detection model and an image to process
Steps
Implement an OCR class based on FastALPR's
BaseOCRinterface.In its
predictmethod, pass the cropped plate image to Tesseract and return anOcrResult.Provide the custom OCR object to
ALPRand callpredictwith an image path.
Expected outcome
The example routes plate text recognition through Tesseract and returns FastALPR results.
Guide limitations
- The repository example uses the
engTesseract language setting; other language settings require adjustment. - The example does not establish recognition performance for particular plates, images, or deployment conditions.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 14 October 2026
- Original website title
- ankandrew/fast-alpr · Official homepage title · retained in its source language