README.md

• Dependencies
  • Debugging missing dependencies
• GPGPU acceleration
• Pre-built binaries
• Building
• Testing
  • Usage
  • Examples

---

This application is a reference implementation for developers to show how to use the C# API and could be used to easily check the accuracy. The C# API is a wrapper around the C++ API defined at https://www.doubango.org/SDKs/anpr/docs/cpp-api.html. A C++ twin sample application is at c++/recognizer.

The application accepts path to a JPEG/PNG/BMP file as input. This is not the recommended way to use the API. We recommend reading the data directly from the camera and feeding the SDK with the uncompressed YUV data without saving it to a file or converting it to RGB.

If you don't want to build this sample and is looking for a quick way to check the accuracy then, try our cloud-based solution at https://www.doubango.org/webapps/alpr/.

This sample is open source and doesn't require registration or license key.

Dependencies

The SDK is developed in C++11 and you'll need glibc 2.27+ on Linux and Visual C++ Redistributable for Visual Studio 2015 (any later version is ok) on Windows. You most likely already have these dependencies on you machine as almost every program require it.

If you're planning to use OpenVINO, then you'll need Intel C++ Compiler Redistributable (choose newest). Please note that OpenVINO is packaged in the SDK as plugin and loaded (dlopen) at runtime. The engine will fail to load the plugin if Intel C++ Compiler Redistributable is missing on your machine but the program will work as expected with Tensorflow as fallback. We highly recommend using OpenVINO to speedup the inference time. See benchmark numbers with/without OpenVINO at https://www.doubango.org/SDKs/anpr/docs/Benchmark.html#core-i7-windows.

Debugging missing dependencies

To check if all dependencies are present:

• Windows x86_64: Use Dependency Walker on binaries/windows/x86_64/ultimateALPR-SDK.dll and binaries/windows/x86_64/ultimatePluginOpenVINO.dll if you're planning to use OpenVINO.
• Linux x86_64: Use ldd <your-shared-lib> on binaries/linux/x86_64/libultimate_alpr-sdk.so and binaries/linux/x86_64/libultimatePluginOpenVINO.so if you're planning to use OpenVINO.

GPGPU acceleration

By default GPGPU acceleration is disabled. Check here for more information on how to enable it.

Pre-built binaries

If you don't want to build this sample by yourself then, use the pre-built C++ versions:

• Windows x86_64: recognizer.exe under binaries/windows/x86_64
• Linux x86_64: recognizer under binaries/linux/x86_64. Built on Ubuntu 18. You'll need to download libtensorflow.so as explained here.
• Linux aarch64: recognizer under binaries/linux/aarch64.
• Raspberry Pi: recognizer under binaries/raspbian/armv7l
• Android: check android folder

On Windows, the easiest way to try this sample is to navigate to binaries/windows/x86_64 and run binaries/windows/x86_64/recognizer.bat. You can edit these files to use your own images and configuration options.

Building

This sample contains a single C# source file.

You'll need Visual Studio to build the code. The VS project is at recognizer.vcxproj. Open it.

• You will need to change the "Command line arguments" like the below image. Default value: --assets "$(ProjectDir)..\..\..\assets" --image "$(ProjectDir)..\..\..\assets\images\lic_us_1280x720.jpg" --charset "latin"

You're now ready to build and run the sample.

Usage

recognizer is a command line application with the following usage:

recognizer \
      --image <path-to-image-with-mrzdata-to-process> \
      [--assets <path-to-assets-folder>] \
      [--charset <recognition-charset:latin/korean/chinese>] \
      [--tokenfile <path-to-license-token-file>] \
      [--tokendata <base64-license-token-data>]

Options surrounded with [] are optional.

• --image Path to the image(JPEG/PNG/BMP) to process. You can use default image at ../../../assets/images/lic_us_1280x720.jpg.
• --assets Path to the assets folder containing the configuration files and models. Default value is the current folder.
• --charset Defines the recognition charset (a.k.a alphabet) value (latin, korean, chinese...). Default: latin.
• --tokenfile Path to the file containing the base64 license token if you have one. If not provided then, the application will act like a trial version. Default: null.
• --tokendata Base64 license token if you have one. If not provided then, the application will act like a trial version. Default: null.

Examples

You'll need to change the Visual Studio properties to define the command line arguments.

recognizer.exe \
    --image "$(ProjectDir)..\..\..\assets\images\lic_us_1280x720.jpg" \
    --assets "$(ProjectDir)..\..\..\assets" \
    --charset "latin"