README.md openalpr OpenALPR is an open source Automatic License Plate Recognition library written in C with bindings in C#, Java, Node.js, Go, and Python. The library analyzes images and video streams to identify license plates.
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The output is the text representation of any license plate characters. Check out a live online demo here: User Guide OpenALPR includes a command line utility. Simply typing 'alpr image file path' is enough to get started recognizing license plate images. For example, the following output is created by analyzing this image: user@linux:/openalpr$ alpr./samplecar.png plate0: top 10 results - Processing Time = 58.1879ms.
PE3R2X confidence: 88.9371 - PE32X confidence: 78.1385 - PE3R2 confidence: 77.5444 - PE3R2Y confidence: 76.1448 - P63R2X confidence: 72.9016 - FE3R2X confidence: 72.1147 - PE32 confidence: 66.7458 - PE32Y confidence: 65.3462 - P632X confidence: 62.1031 - P63R2 confidence: 61.5089 Detailed command line usage: user@linux:/openalpr$ alpr -help USAGE: alpr -c -config -n -seek -p -clock -d -j - -version -h Where: -c, -country Country code to identify (either us for USA or eu for Europe). Default=us -config Path to the openalpr.conf file -n, -topn Max number of possible plate numbers to return. Default=10 -seek Seek to the specified millisecond in a video file. Default=0 -p, -pattern Attempt to match the plate number against a plate pattern (e.g., md for Maryland, ca for California) -clock Measure/print the total time to process image and all plates. Default=off -d, -detectregion Attempt to detect the region of the plate image.
Experimental Default=off -j, -json Output recognition results in JSON format. Default=off -, -ignorerest Ignores the rest of the labeled arguments following this flag.version Displays version information and exits.h, -help Displays usage information and exits. Image containing license plates OpenAlpr Command Line Utility Binaries Pre-compiled Windows binaries can be downloaded on the Install OpenALPR on Ubuntu 16.04 with the following commands: sudo apt-get update && sudo apt-get install -y openalpr openalpr-daemon openalpr-utils libopenalpr-dev Documentation Detailed documentation is available at Integrating the Library OpenALPR is written in C and has bindings in C#, Python, Node.js, Go, and Java. Please see this guide for examples showing how to run OpenALPR in your application: Compiling OpenALPR compiles and runs on Linux, Mac OSX and Windows.
OpenALPR requires the following additional libraries: - Tesseract OCR v3.0.4 (- OpenCV v2.4.8+ (After cloning this GitHub repository, you should download and extract Tesseract and OpenCV source code into their own directories. Compile both libraries. Please follow these detailed compilation guides for your respective operating system:. If all went well, there should be an executable named alpr along with libopenalpr-static.a and libopenalpr.so that can be linked into your project. # Build docker image docker build -t openalpr # Download test image wget # Run alpr on image docker run -it -rm -v $(pwd ):/data:ro openalpr -c eu h786poj.jpg Questions Please post questions or comments to the Google group list: Contributions Improvements to the OpenALPR library are always welcome. Please review the and get started.
Code contributions are not the only way to help out. Do you have a large library of license plate images? If so, please upload your data to the anonymous FTP located at upload.openalpr.com.
Do you have time to 'tag' plate images in an input image or help in other ways? Please let everyone know by posting a note in the forum. License Affero GPLv3 Commercial-friendly licensing available.
Install OpenALPR on Raspberry PI 3 31 August 2016 on,!!WARNING!! It seems there are some problems with using the latest Tesseract code base. For mor details please see the comments! I wrote a now post about this topic. You can check it on this link: Before you start installing OpenALPR I suggest you to go through this and the mentioned post first. They may contain a lot of useful information.
In this tutorial I will show how can you install on you Raspberry PI 3. From its home page: OpenALPR is an open source Automatic License Plate Recognition library written in C with bindings in C#, Java, Node.js, Go, and Python. The library analyzes images and video streams to identify license plates. The output is the text representation of any license plate characters. So after successfully installation of OpenALPR you Raspberry will be able to recognize License Plates from a single photo or from live stream. Please note that in your country maybe illegal to use this tool on public or even for private use, therefore I use it only for my entertainment.
Lets Begin.:) 0. What is needed?. Raspberry Image: 2016-05-27-raspbian-jessie.img. At least 8GB 16GB microSD card to flash the image.
Raspberry PI 2 or 3 (I do not advise RPI 1 because I think it is too slow, and image processing will also be slow, and the compiling process will take much longer) 1. Update & Upgrade Before you start installing alpr and its dependencies update your Raspbian. I use a vanilla image so it is must to update. Comment: I will do evry step with root access. (Without root access you are lost, or at least the install process will be much harder.) This Raspberry is only for this project thus I don't have to care about loosing anything, or installing packages which overrides other projects. Run the following commands: apt-get update apt-get upgrade 2.
Install the necessary packages Previously I gathered all of the packages are needed to compile alpr and all of its dependencies.
= Automatic Licence Plate Recognition System Compilation and installation This project uses rake to build and install the application 'rake' - This will compile the application 'rake setup' - This will download sample images 'rake run' - This will run the application with downloaded images Required Dependencies The ALPR system depends on several other libraries that you will need install, if they're not already present on your system. OpenCV computer vision library. Tesseract OCR Links OpenCV - OpenCV Wiki - Tesseract OCR.
In this tutorial I show how to use the Tesseract - Optical Character Recognition (OCR) in conjunction with the OpenCV library to detect text on a license plate recognition application. Tesseract is an optical character recognition engine for various operating systems. It is free software, released under the Apache License, Version 2.0, and development has been sponsored by Google since 2006. Tesseract is considered one of the most accurate open source OCR engines currently available.
The Tesseract engine was originally developed as proprietary software at Hewlett Packard labs in Bristol, England and Greeley, Colorado between 1985 and 1994, with some more changes made in 1996 to port to Windows, and some migration from C to C in 1998. A lot of the code was written in C, and then some more was written in C. Since then all the code has been converted to at least compile with a C compiler. Very little work was done in the following decade. It was then released as open source in 2005 by Hewlett Packard and the University of Nevada, Las Vegas (UNLV).
Tesseract development has been sponsored by Google since 2006. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code. The library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. These algorithms can be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce a high resolution image of an entire scene, find similar images from an image database, remove red eyes from images taken using flash, follow eye movements, recognize scenery and establish markers to overlay it with augmented reality, etc.
OpenCV has more than 47 thousand people in their user community and an estimated number of downloads exceeding 7 million. The library is used extensively in companies, research groups and by governmental bodies. Email: [email protected] twitter: git.
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