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This application is used as reference code for developers to show how to use the C++ API and could be used to easily check the accuracy. 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/mrz/.

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

Pre-built binaries

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

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 this file to use your own images and configuration options.

Building

This sample contains a single C++ source file and is easy to build. The documentation about the C++ API is at https://www.doubango.org/SDKs/mrz/docs/cpp-api.html.

Android

Please check android folder for Android samples.

iOS

Please check iOS folder for iOS samples.

Windows

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

  1. You will need to change the "Command Arguments" like the below image. Default value: --image $(ProjectDir)..\..\..\assets\images\Czech_passport_2005_MRZ_orient1_1300x1002.jpg --assets $(ProjectDir)..\..\..\assets
  2. You will need to change the "Environment" variable like the below image. Default value: PATH=$(VCRedistPaths)%PATH%;$(ProjectDir)..\..\..\binaries\windows\x86_64

VC++ config

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

Generic GCC

Next command is a generic GCC command:

cd ultimateMRZ-SDK/samples/c++/recognizer

g++ main.cxx -O3 -I../../../c++ -L../../../binaries/<yourOS>/<yourArch> -lultimate_mrz-sdk -o recognizer
  • You've to change yourOS and yourArch with the correct values. For example, on Linux x86_64 they would be equal to linux and x86_64 respectively.
  • If you're cross compiling then, you'll have to change g++ with the correct triplet. For example, on Android ARM64 the triplet would be equal to aarch64-linux-android-g++.

Raspberry Pi (Raspbian OS)

To build the sample for Raspberry Pi you can either do it on the device itself or cross compile it on Windows, Linux or OSX machines. For more information on how to install the toolchain for cross compilation please check here.

cd ultimateMRZ-SDK/samples/c++/recognizer

arm-linux-gnueabihf-g++ main.cxx -O3 -I../../../c++ -L../../../binaries/raspbian/armv7l -lultimate_mrz-sdk -o recognizer
  • On Windows: replace arm-linux-gnueabihf-g++ with arm-linux-gnueabihf-g++.exe
  • If you're building on the device itself: replace arm-linux-gnueabihf-g++ with g++ to use the default GCC

Testing

After building the application you can test it on your local machine.

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>] \
      [--backprop <whether-to-enable-backpropagation:true/false>] \
      [--vcheck <whether-to-enable-vertical-check:true/false>] \
      [--ielcd <whether-to-enable-IELCD:true/false>] \
      [--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/Czech_passport_2005_MRZ_orient1_1300x1002.jpg.
  • --assets Path to the assets folder containing the configuration files and models. Default value is the current folder.
  • --backprop Whether to enable backpropagation to detect the MRZ lines. More information at https://www.doubango.org/SDKs/mrz/docs/Detection_techniques.html#backpropagation. Default: true for x86 CPUs and false for ARM CPUs.
  • --vcheck Whether to enable vertical check to detect +/-90deg rotated images. Default: true for x86 CPUs and false for ARM CPUs.
  • --ielcd Whether to enable Image Enhancement for Low Contrast Document (IELCD). More information at https://www.doubango.org/SDKs/mrz/docs/IELCD.html#ielcd. Default: true for x86 CPUs and false for ARM CPUs.
  • --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

For example, on Raspberry Pi you may call the recognizer application using the following command:

LD_LIBRARY_PATH=../../../binaries/raspbian/armv7l:$LD_LIBRARY_PATH ./recognizer \
    --image ../../../assets/images/Czech_passport_2005_MRZ_orient1_1300x1002.jpg \
    --assets ../../../assets \
    --backprop false --vcheck false --ielcd false

On Linux x86_64, you may use the next command:

LD_LIBRARY_PATH=../../../binaries/linux/x86_64:$LD_LIBRARY_PATH ./recognizer \
    --image ../../../assets/images/Czech_passport_2005_MRZ_orient1_1300x1002.jpg \
    --assets ../../../assets \
    --backprop true --vcheck true --ielcd true

On Windows x86_64, you may use the next command:

recognizer.exe ^
    --image ../../../assets/images/Czech_passport_2005_MRZ_orient1_1300x1002.jpg ^
    --assets ../../../assets ^
    --backprop true --vcheck true --ielcd true

Please note that if you're cross compiling the application then you've to make sure to copy the application and both the assets and binaries folders to the target device.