license-plate-recognition

License Plate Recognition

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Overview

License Plate Recognition (LPR) is a Metro Analytics use case that locates vehicle license plates in a video stream and reads their alphanumeric content. The pipeline composes two specialized models:

• License Plate Detector -- yolov8_license_plate_detector, a YOLOv8 model fine-tuned to localize license plates as oriented bounding boxes.
• OCR Recognizer -- ch_PP-OCRv4_rec_infer, the PaddleOCR PP-OCRv4 multilingual text recognizer that converts each cropped plate into a text string.

Typical Metro deployments include:

• Tolling and Access Control -- read plates at gates, depots, and parking entries.
• Vehicle Search and Forensics -- index plates seen at a station for investigative lookup.
• Fleet and Bus Monitoring -- correlate detected plates with operational schedules.

The detector returns one bounding box per plate; the OCR stage runs as a downstream classifier on the cropped region, attaching the recognized string as inference metadata on the frame.

Note: Plate detector accuracy depends on the regional distribution of training data. The bundled detector was trained primarily on European and US plates. For other regions, fine-tune the YOLOv8 detector on a representative dataset.

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Prerequisites

• Python 3.11+
• Install OpenVINO (latest version)
• Install Intel DLStreamer

Create and activate a Python virtual environment before running the scripts:

python3 -m venv .venv --system-site-packages
source .venv/bin/activate

Note: The --system-site-packages flag is required so the virtual environment can access the system-installed OpenVINO and DLStreamer Python packages.

Activate the OpenVINO and DLStreamer runtimes in the same shell. The DLStreamer Python module is not on sys.path by default, so export PYTHONPATH as well:

source /opt/intel/openvino_2026/setupvars.sh
source /opt/intel/dlstreamer/scripts/setup_dls_env.sh
export PYTHONPATH=/opt/intel/dlstreamer/python:\
/opt/intel/dlstreamer/gstreamer/lib/python3/dist-packages:${PYTHONPATH:-}

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Getting Started

Download the Models and Sample Video

Run the provided script to download the license plate detector and OCR recognizer OpenVINO IR models and the sample test video:

chmod +x export_and_quantize.sh
./export_and_quantize.sh

The script performs the following steps:

1. Downloads the sample test video (ParkingVideo.mp4) from the Intel Edge AI Resources project into the current directory.
2. Downloads the license-plate-reader archive from the Intel Edge AI Resources project and extracts it under ./models/yolov8_license_plate_detector/license-plate-reader/. The archive bundles both the YOLOv8 plate detector (models/yolov8n/yolov8n_retrained.xml, FP32) and the converted PaddleOCR recognizer (models/ch_PP-OCRv4_rec_infer/ch_PP-OCRv4_rec_infer.xml, FP32), so no separate OCR download step is required.

Both IRs are used as-is at FP32 -- no quantization step is performed.

Locating the OCR Recognizer

The PaddleOCR recognizer ships inside the same archive:

./models/yolov8_license_plate_detector/license-plate-reader/models/ch_PP-OCRv4_rec_infer/ch_PP-OCRv4_rec_infer.xml

Note: PaddleOCR PP-OCRv4 is a CTC sequence model. DLStreamer 2026.0+ auto-derives the CTC decoder for the bundled ch_PP-OCRv4_rec_infer IR and exposes the decoded plate string as tensor.label() on each classified ROI -- no external model-proc is required for this sample. For other PaddleOCR variants or non-Latin character sets, supply a custom model-proc (see DLStreamer model_proc reference) with the matching character dictionary.

DLStreamer Sample

The sample below builds the two-stage detection plus OCR pipeline using the Python GStreamer bindings. It mirrors the structure of the upstream DLStreamer license_plate_recognition.sh sample: decodebin3 ! queue ! gvadetect ! queue ! videoconvert ! gvaclassify ! queue ! gvawatermark ! .... The gvadetect element runs the license plate detector; gvaclassify then runs the PaddleOCR recognizer on each detected plate region. A buffer probe extracts the recognized text from the inference metadata attached to each frame. The input is ParkingVideo.mp4, the short parking-lot clip downloaded by export_and_quantize.sh into the current directory. The annotated stream is muxed into output_dlstreamer.mp4 with H.264 (OpenH264).

import os

import gi

gi.require_version("Gst", "1.0")
gi.require_version("GstVideo", "1.0")
from gi.repository import Gst
from gstgva import VideoFrame

Gst.init(None)

MODELS_DIR = os.path.abspath("./models/yolov8_license_plate_detector")
DETECTOR_XML = (
    f"{MODELS_DIR}/license-plate-reader/models/"
    "yolov8n/yolov8n_retrained.xml"
)
OCR_XML = (
    f"{MODELS_DIR}/license-plate-reader/models/"
    "ch_PP-OCRv4_rec_infer/ch_PP-OCRv4_rec_infer.xml"
)
INPUT_VIDEO = "ParkingVideo.mp4"
DEVICE = "GPU"

pipeline_str = (
    f"filesrc location={INPUT_VIDEO} ! decodebin3 ! "
    f"videoconvert ! queue ! "
    f"gvadetect model={DETECTOR_XML} device={DEVICE} ! queue ! "
    f"videoconvert ! "
    f"gvaclassify model={OCR_XML} device={DEVICE} ! queue ! "
    f"gvawatermark ! videoconvert ! video/x-raw,format=I420 ! "
    f"openh264enc ! h264parse ! "
    f"mp4mux ! filesink name=sink location=output_dlstreamer.mp4"
)

pipeline = Gst.parse_launch(pipeline_str)


def on_buffer(pad, info):
    buf = info.get_buffer()
    caps = pad.get_current_caps()
    frame = VideoFrame(buf, caps=caps)
    for region in frame.regions():
        rect = region.rect()
        text = ""
        for tensor in region.tensors():
            if tensor.is_detection():
                continue
            try:
                text = tensor.label() or ""
            except RuntimeError:
                continue
            if text:
                break
        if text:
            print(f"Plate: {text}  bbox=({rect.x},{rect.y})", flush=True)
    return Gst.PadProbeReturn.OK


sink = pipeline.get_by_name("sink")
sink_pad = sink.get_static_pad("sink")
sink_pad.add_probe(Gst.PadProbeType.BUFFER, on_buffer)

pipeline.set_state(Gst.State.PLAYING)
bus = pipeline.get_bus()
bus.timed_pop_filtered(
    Gst.CLOCK_TIME_NONE,
    Gst.MessageType.EOS | Gst.MessageType.ERROR,
)
pipeline.set_state(Gst.State.NULL)

To run on CPU, change DEVICE = "GPU" to DEVICE = "CPU". For NPU, change DEVICE = "GPU" to DEVICE = "NPU" and use batch-size=1 and nireq=4 for best utilization.

Try It on a Sample Video

export_and_quantize.sh already downloaded ParkingVideo.mp4 into the current directory, so the sample is ready to run. Execute the DLStreamer sample above. The annotated video is saved to output_dlstreamer.mp4 with green bounding boxes drawn by gvawatermark around every detected plate. The buffer probe prints one line per detected plate per frame.

Each detected plate that the OCR stage successfully decodes prints one line per frame, for example:

Plate: 9MRM624  bbox=(979,458)

Low-confidence ROIs (small, blurred, or partially occluded plates) may yield an empty CTC decode and are filtered out by the probe.

If you only need the structured output and not the annotated video, replace filesink with fakesink in pipeline_str and pipe the console output to a file.

Known warning: The openh264enc element prints [OpenH264] this = 0x..., Error:CWelsH264SVCEncoder::EncodeFrame(), cmInitParaError. on the first frame. This is a benign initialization message — the output video is encoded correctly. The warning comes from the OpenH264 library's internal logging and does not indicate a real error.

Expected Output

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License

Copyright (C) Intel Corporation. All rights reserved. Licensed under the MIT License. See LICENSE for details.

References

• DLStreamer License Plate Recognition Sample
• Intel Edge AI Resources -- License Plate Reader Model
• PaddleOCR PP-OCRv4
• Ultralytics YOLOv8 Documentation
• OpenVINO Documentation
• Intel DLStreamer