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Nomeroff Net. Automatic numberplate recognition system. Version 3.4.1
Now there is a war going on in our country, russian soldiers are shooting at civilians in Ukraine. Enemy aviation launches rockets and drops bombs on residential quarters.
We are deeply thankful for the unprecedented wave of support for Ukraine from around the world. Below is a list of funds that help the Ukrainian army in the fight against Russian invaders:
Nomeroff Net is an opensource python license plate recognition framework based on YOLOv5 and CRAFT networks and customized OCR-module powered by RNN architecture.
Write to us if you are interested in helping us in the formation of a dataset for your country.
Nomeroff Net requires Python >= 3.7
git clone https://github.com/ria-com/nomeroff-net.git cd nomeroff-net
# for Opencv yum install libSM # for pycocotools install yum install python3-devel # ensure that you have installed gcc compiler yum install gcc yum install git # Before "yum install ..." download https://libjpeg-turbo.org/pmwiki/uploads/Downloads/libjpeg-turbo.repo to /etc/yum.repos.d/ yum install libjpeg-turbo-official
pip3 install -r requirements.txt
# ensure that you have installed gcc compiler apt-get install gcc # for opencv install apt-get install -y libglib2.0 apt-get install -y libgl1-mesa-glx # for pycocotools install (Check the name of the dev-package for your python3) apt-get install python3.7-dev # other packages apt-get install -y git apt-get install -y libturbojpeg
pip3 install -r requirements.txt
from nomeroff_net import pipeline from nomeroff_net.tools import unzip number_plate_detection_and_reading = pipeline("number_plate_detection_and_reading", image_loader="opencv") (images, images_bboxs, images_points, images_zones, region_ids, region_names, count_lines, confidences, texts) = unzip(number_plate_detection_and_reading(['./data/examples/oneline_images/example1.jpeg', './data/examples/oneline_images/example2.jpeg'])) print(texts)
Note: This example disables some important Nomeroff Net features. It will recognize numbers that are photographed in a horizontal position.
from nomeroff_net import pipeline from nomeroff_net.tools import unzip number_plate_detection_and_reading = pipeline("number_plate_short_detection_and_reading", image_loader="opencv") (images, images_bboxs, zones, texts) = unzip(number_plate_detection_and_reading(['./data/examples/oneline_images/example1.jpeg', './data/examples/oneline_images/example2.jpeg'])) print(texts) # (['AC4921CB'], ['RP70012', 'JJF509'])
In order to evaluate the quality of work of Nomeroff Net without spending time on setting up and installing, we made an online form in which you can upload your photo and get the recognition result online
All data on the basis of which the training was conducted is provided by RIA.com. In the following, we will call this data the AUTO.RIA Numberplate Dataset.
We will be grateful for your help in the formation and layout of the dataset with the image of the license plates of your country. For markup, we recommend using VGG Image Annotator (VIA)
The system uses several neural networks. One of them is the classifier of numbers at the post-processing stage. It uses dataset AUTO.RIA Numberplate Options Dataset.
The categorizer accurately (99%) determines the country and the type of license plate. Please note that now the classifier is configured mainly for the definition of Ukrainian numbers, for other countries it will be necessary to train the classifier with new data.
As OCR, we use a specialized implementation of a neural network with RNN layers, for which we have created several datasets:
If we did not manage to update the link on dataset you can find the latest version here
This gives you the opportunity to get 99% accuracyon photos that are uploaded to AUTO.RIA project
Contributions to this repository are welcome. Examples of things you can contribute: