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Fast Plate Ocr

Fast Plate Ocr is a Python library of lightweight OCR models and tools for recognizing text in cropped vehicle license-plate images.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Fast Plate Ocr — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it at the text-recognition stage when another part of the computer-vision pipeline has already located and cropped a license plate.

Who it is for

  • Developers adding license-plate text recognition after a plate detector.
  • Computer-vision teams building workflows for vehicle or parking management.
  • Practitioners who want to run pretrained OCR models or fine-tune them on their own plate dataset.

Documented uses

  • Extract plate text from a cropped image file.
  • Use a pretrained model for inference through an ONNX Runtime backend.
  • Train an OCR model from scratch or fine-tune a pretrained model on a plate dataset.
  • Obtain region predictions when using a model with a region head and configured plate regions.
Before you choose

Requirements & limits

The repository is public and specifies the MIT license.

  • Input images should be cropped plates; plate detection is a separate preceding task.
  • Inference requires installing an ONNX Runtime backend because none is installed by default.
  • Training dependencies are installed separately with the train extra.
  • Training supports Keras 3 with TensorFlow, JAX or PyTorch backends; documented export formats include ONNX, CoreML and TFLite.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Run OCR on a cropped plate image

Use a pretrained model to recognize text in a plate image stored on disk.

Before you start

  • Python environment with fast-plate-ocr installed.
  • An ONNX Runtime backend installed through an appropriate package extra; the README's CPU option is fast-plate-ocr[onnx].
  • A cropped license-plate image file.

Steps

  1. Install fast-plate-ocr with an ONNX backend appropriate to the intended platform.

  2. Import LicensePlateRecognizer from fast_plate_ocr.

  3. Create a recognizer with a model name such as cct-s-v2-global-model.

  4. Call run with the path to the cropped plate image and inspect the prediction.

Expected outcome

The recognizer returns a prediction for the plate text. With a model that has a region head and a defined plate_regions configuration, predictions can also include a region; return_confidence=True populates region_prob.

Guide limitations

  • The OCR expects cropped plate images, so plate detection is a preceding stage.
  • No ONNX runtime is installed by default; an ONNX backend must be installed for inference.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Fast Plate OcrExtract plate text from a cropped image file.Linux, macOS, WindowsThe repository is public and specifies the MIT license.
Gemma3Moe BuilderConvert a dense Gemma-3-270m checkpoint into a model with a router and configurable expert count and top-k selection.CLI, Linux, macOS, Self-hostedThe repository documents local installation as a Python package and requires Python 3.10 or later. Training and routing tools require the optional `datasets` dependency.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
ankandrew/fast-plate-ocr · Official homepage title · retained in its source language
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