Package: nomeroff-net
Nomeroff Net. Automatic numberplate recognition system. Version 3.1Now there is a war going on in my country, Russian soldiers are shooting at civilians in Ukraine. Enemy aviation launches rockets and drops bombs on residential quarters.
For 2 weeks we have been appealing to USA & Nato: "Please close the sky", but so far these calls remain unanswered...IntroductionNomeroff 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.Change History.InstallationInstallation from Source (Linux)Nomeroff Net requires Python >= 3.7Clone Projectgitclonehttps://github.com/ria-com/nomeroff-net.gitcdnomeroff-netFor Centos, Fedora and other RedHat-like OS:# for OpencvyuminstalllibSM# for pycocotools installyuminstallpython3-devel# ensure that you have installed gcc compileryuminstallgcc

yuminstallgit# Before "yum install ..." download https://libjpeg-turbo.org/pmwiki/uploads/Downloads/libjpeg-turbo.repo to /etc/yum.repos.d/yuminstalllibjpeg-turbo-officialinstall requirements:pip3install-rrequirements.txtFor Ubuntu and other Debian-like OS:# ensure that you have installed gcc compilerapt-getinstallgcc# for opencv installapt-getinstall-ylibglib2.0
apt-getinstall-ylibgl1-mesa-glx# for pycocotools install (Check the name of the dev-package for your python3)apt-getinstallpython3.7-dev# other packagesapt-getinstall-ygit
apt-getinstall-ylibturbojpeginstall requirements:pip3install-rrequirements.txtHello Nomeroff Netfromnomeroff_netimportpipelinefromnomeroff_net.toolsimportunzipnumber_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)Hello Nomeroff Net for systems with a small GPU size.Note: This example disables some important Nomeroff Net features. It will recognize numbers that are photographed in a horizontal position.fromnomeroff_netimportpipelinefromnomeroff_net.toolsimportunzipnumber_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'])More ExamplesOnline DemoIn 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 therecognition result onlineAUTO.RIA Numberplate DatasetAll data on the basis of which the training was conducted is provided by RIA.com.
In the following, we will call this data theAUTO.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 usingVGG Image Annotator (VIA)Dataset Example:AUTO.RIA Numberplate Options DatasetThe system uses several neural networks. One of them is the classifier of numbers at the post-processing stage. It uses datasetAUTO.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.AUTO.RIA Numberplate OCR DatasetsAs OCR, we use a specialized implementation of a neural network with RNN layers,
for which we have created several datasets:AUTO.RIA Numberplate OCR UA Dataset (Ukrainian)AUTO.RIA Numberplate OCR UA Dataset (Ukrainian) with old design DatasetAUTO.RIA Numberplate OCR EU Dataset (European)AUTO.RIA Numberplate OCR RU Dataset (Russian)AUTO.RIA Numberplate OCR KZ Dataset (Kazakh)AUTO.RIA Numberplate OCR GE Dataset (Georgian)AUTO.RIA Numberplate OCR BY Dataset (Belarus)AUTO.RIA Numberplate OCR SU Dataset (exUSSR)AUTO.RIA Numberplate OCR KG Dataset (Kyrgyzstan)AUTO.RIA Numberplate OCR AM Dataset (Armenia)If we did not manage to update the link on dataset you can find the latest versionhereThis gives you the opportunity to get99% accuracyon photos that are uploaded toAUTO.RIAprojectContributingContributions to this repository are welcome. Examples of things you can contribute:Training on other datasets.Accuracy Improvements.CreditsDmytro Probachay <dmytro.probachay@ria.com>Oleg Cherniy <oleg.cherniy@ria.com>LinksNomeroff Net project siteGitHub repositoryNumberplate recognition. Practical guide. Part 1 (in Russian)Numberplate recognition. As we got 97% accuracy for Ukrainian numbers. Part 2 (in Russian)