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.
Check out a live online demo here: http://www.openalpr.com/demo-image.html
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 <country_code>] [--config <config_file>] [-n <topN>] [--seek <integer_ms>] [-p <pattern code>] [--clock] [-d] [-j] [--] [--version] [-h] <image_file_path> Where: -c <country_code>, --country <country_code> Country code to identify (either us for USA or eu for Europe). Default=us --config <config_file> Path to the openalpr.conf file -n <topN>, --topn <topN> Max number of possible plate numbers to return. Default=10 --seek <integer_ms> Seek to the specified millisecond in a video file. Default=0 -p <pattern code>, --pattern <pattern code> 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, --detect_region Attempt to detect the region of the plate image. [Experimental] Default=off -j, --json Output recognition results in JSON format. Default=off --, --ignore_rest Ignores the rest of the labeled arguments following this flag. --version Displays version information and exits. -h, --help Displays usage information and exits. <image_file_path> Image containing license plates OpenAlpr Command Line Utility
Pre-compiled Windows binaries can be downloaded on the releases page
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
Detailed documentation is available at doc.openalpr.com
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: http://doc.openalpr.com/bindings.html
OpenALPR compiles and runs on Linux, Mac OSX and Windows.
OpenALPR requires the following additional libraries:
- Tesseract OCR v3.0.4 (https://github.com/tesseract-ocr/tesseract) - OpenCV v2.4.8+ (http://opencv.org/)
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 https://github.com/openalpr/openalpr.git # Download test image wget http://plates.openalpr.com/h786poj.jpg # Run alpr on image docker run -it --rm -v $(pwd):/data:ro openalpr -c eu h786poj.jpg
Please post questions or comments to the Google group list: https://groups.google.com/forum/#!forum/openalpr
Improvements to the OpenALPR library are always welcome. Please review the OpenALPR design description 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.
Commercial-friendly licensing available. Contact: email@example.com