Nomeroff Net

Nomeroff Net. Automatic numberplate recognition system. Version 3.4.1

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Introduction

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.

Change History.

Installation

Installation from Source (Linux)

Nomeroff Net requires Python >= 3.7

Clone Project

git clone https://github.com/ria-com/nomeroff-net.git
cd nomeroff-net

For Centos, Fedora and other RedHat-like OS:

# 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

install requirements:

pip3 install -r requirements.txt

For Ubuntu and other Debian-like OS:

# 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

install requirements:

pip3 install -r requirements.txt

Hello Nomeroff Net

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)

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.

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'])

More Examples

Online Demo

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

AUTO.RIA Numberplate Dataset

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)

Dataset Example:

AUTO.RIA Numberplate Options Dataset

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.

AUTO.RIA Numberplate OCR Datasets

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

Contributing

Contributions to this repository are welcome. Examples of things you can contribute:

Training on other datasets.
Accuracy Improvements.

Credits

Dmytro Probachay <dmytro.probachay@ria.com>
Oleg Cherniy <oleg.cherniy@ria.com>

pubpy2015/nomeroff-net