YOLOv8-MRZ is a deep learning model for object detection specifically designed to recognize machine-readable zones (MRZ) in identification documents. This model is capable of detecting and extracting relevant information from passports, driver’s licenses, and other types of identity documents. In this tutorial, we will guide you through the steps to use the YOLOv8-MRZ model in an Android application.
Step 1: Setup your development environment
Before we start coding, make sure you have the following prerequisites installed on your development machine:
- Android Studio
- Java Development Kit (JDK)
- Android SDK
- Git
- Python
- TensorFlow library
- YOLOv8-MRZ model weights and configuration files
Step 2: Clone the YOLOv8-MRZ repository
First, clone the YOLOv8-MRZ repository from GitHub by running the following command in your terminal:
git clone https://github.com/sergiomsilva/mrz-android-demo.gitStep 3: Download model files
Download the YOLOv8-MRZ model weights and configuration files from the following link: https://github.com/sergiomsilva/mrz-android-demo/releases/tag/yolov8-mrz. Copy these files into the app/src/main/assets directory of the cloned repository.
Step 4: Import the project into Android Studio
Open Android Studio and import the cloned project by selecting File > Open and navigating to the project directory. Wait for the project to sync and build.
Step 5: Modify the UI
You can make modifications to the UI as needed. The demo app provided in the repository has a simple interface with a camera preview and a button to capture an image.
Step 6: Implement the YOLOv8-MRZ model in Android
To implement the YOLOv8-MRZ model in Android, you need to add the TensorFlow Lite library to your project. You can do this by adding the following dependency in your app’s build.gradle file:
implementation 'org.tensorflow:tensorflow-lite:3.4.0'Next, create a class to handle model inference. You can use the provided TFLiteObjectDetectionAPIModel.java class from the repository as a reference. This class loads the model files and performs object detection on input images.
Step 7: Handle camera input
You need to implement functionality to capture images from the camera and pass them to the YOLOv8-MRZ model for inference. You can use the CameraX library provided by Android to simplify camera integration.
Step 8: Display results
Once the YOLOv8-MRZ model has processed the input image, extract and display the detected MRZ information on the screen. You can use TextView or other UI elements to show the extracted text.
Step 9: Test the application
Build and run the application on an Android device or emulator. Test the functionality by capturing images of identification documents containing MRZ zones. Verify that the model can detect and extract the MRZ information accurately.
Conclusion
In this tutorial, we have covered the steps to use the YOLOv8-MRZ model in an Android application for detecting machine-readable zones in identification documents. By following these steps and customizing the source code provided in the repository, you can create your own MRZ recognition app. Experiment with different settings, image preprocessing techniques, and model optimizations to improve the accuracy and performance of your application.
