Building Smart Applications: A Beginner's Guide to ML Kit in Android

ML Kit: A Developer-Friendly Solution for AI Integration

AI is revolutionizing Android apps, enhancing their intelligence and usability. Google's ML Kit streamlines AI integration, enabling developers to easily add text recognition, image labeling and object detection.

Google's ML Kit is a powerful, developer-friendly machine learning library for Android and iOS applications. It simplifies integrating AI-powered features into your apps, enabling developers to deliver more innovative and engaging user experiences. With ML Kit, you don't need extensive knowledge of machine learning or AI; its pre-built models and APIs allow you to add sophisticated functionalities like text recognition, face detection, barcode scanning, and more with just a few lines of code.

What makes ML Kit particularly appealing is its flexibility. It offers both on-device and cloud-based APIs, allowing you to choose between faster offline performance and more computationally intensive, cloud-powered capabilities based on your app's needs. Additionally, ML Kit supports custom model integration, enabling developers to deploy their machine learning models for unique use cases.

Whether you're looking to build innovative features or improve user engagement, ML Kit provides the tools and scalability to make your app more creative and impactful. By abstracting the complexities of machine learning, ML Kit empowers developers to focus on delivering value and innovation to their users.

Advantages and Benefits of ML Kit for Android Developers

ML Kit simplifies AI integration for Android developers, offering a range of pre-trained models and flexible deployment options. Here's why it stands out:

• Pre-built Models – Use ML Kit's optimized models for text recognition, face detection, and more.
• On-Device & Cloud Processing – Balance speed, accuracy, and resource usage.
• No ML Expertise Required – Beginner-friendly APIs allow seamless AI integration.
• Cross-Platform Support – Works on Android and iOS.
• Custom Model Support – Import TensorFlow Lite models for advanced AI scenarios.

What is ML Kit?

• Definition: ML Kit provides a collection of pre-trained machine learning APIs that enhance mobile applications with AI-powered functionalities.
• Features:
  • Text Recognition.
  • Face Detection.
  • Barcode Scanning.
  • Image Labelling.
  • Pose Detection.
  • Language Identification and Translation.

Setting Up ML Kit in Android Studio

Step 1: Add Dependencies for ML Kit. 

Update your build,gradle file to include the required ML Kit libraries.

Example for Text and Image Recognition:

Step 2: Configure Permissions in the manifest file

Ensure that necessary permissions such as camera access are added.

Step 3: Add the provider in the manifest file

Define the required provider for secure file handling.

Step 4: Create res/xml/file_paths.xml:

Define file paths for accessing images from storage.

Step 5: Initialize ML Kit in Your Project

Import the necessary classes and initialize ML Kit components.

Then, add the parameter to the client.

Step 6: Add the objects for the gallery and camera launcher

Define objects to handle image selection from the gallery and camera.

Step 7: Implement the camera and gallery launchers in onCreate

Set up image selection functionality.

Step 8: Set the tab layout.

Ensure TabLayout is properly configured after attaching the adapter to the ViewPager.

Step 9: Implement button click to capture image from the camera

Add functionality to capture images using the device camera.

Step 10: Implement Button Click to select Image from the Gallery

Enable users to select images from their gallery.

Step 11: Add the buttons for the camera and gallery in the XML file:

Design UI elements for image selection.

Step 12: Add the TabLayout and ViewPager in the XML file:

Ensure proper layout structure for smooth navigation.

Step 13: Add tab_selector file in res/drawable:

Customize tab appearance using a selector file.

Step 14: Add background_gradient file in res/drawable:

Enhance UI aesthetics with a gradient background.

Step 15: Add the colors in res/values/colors:

Define the required color values.

Step 16: Add the following code for Text Recognition in TextRecognitionFragment:

Add the necessary ML Kit logic for text recognition.

Step 17: Add the following code for Object Detection in ObjectDetectionFragment:

Integrate ML Kit's object detection functionality.

Step 18: Create a file for ViewPagerAdapter

Implement the ViewPager adapter for tab navigation.

Please find the screenshots of the app screens below