Creating Text Detector in Android using Firebase ML Kit


The Android Firebase ML Kit's text detection tool makes it easier to get text from an image, which helps apps like OCR, paper scanning, and augmented reality. This guide shows how to add a text analysis to Android apps.

Setting up The Development Environment

Set up an Android text detector to work with Firebase ML Kit by running Android Studio and creating a project. Here are steps shown below to do so.

Installing Android Studio

  • Downloading and installing Android Studio

  • Setting up the Android SDK

  • Configuring virtual devices for testing

Creating a New Android Project

  • Creating a new project in Android Studio

  • Configuring project settings and dependencies

  • Setting up the application layout and resources

Including Prerequisites For The Firebase ML Kit

  • Adding dependencies and libraries for Firebase ML Kit.

  • Setting up the Firebase ML Kit in your project

Configuring Firebase ML Kit

Set up a Firebase project, turn on ML Kit, and add it to your Android app so it can recognize text. Follow the below steps to do the to configure the Firebase ML kit.

Creating a Firebase Project

  • Creating a new Firebase project in the Firebase console

  • Linking the project to your Android application

Turning on ML Kit in Firebase's Administration Panel

  • Going into the Firebase dashboard and finding the ML Kit section

  • Turning on the ML Kit API and features

Building an Android App With Firebase ML Kit

  • Connecting the Android project to the Firebase project

  • Verifying the integration with Firebase ML Kit

  • Configuring the project's requirements on Firebase ML Kit

Implementing Text Detection Functionality

You may implement text detection in your Android app now as the necessary tools, like Firebase ML Kit, are available. We will install Firebase ML Kit TextRecognizer, take some images, analyze them, extract the text, and deal with the results.

Initializing Firebase ML Kit TextRecognizer

  • Creating an instance of the TextRecognizer

  • Configuring text recognition settings

Capturing And Processing Images

  • Implementing the camera functionality for image capture

  • Handling image orientation and aspect ratio

  • Preprocessing the captured image for better text recognition

Extracting Text From Images

  • Feeding the image to the TextRecognizer for text extraction

  • Obtaining the recognized text and associated metadata

Handling Text Recognition Results

  • Processing the recognized text and displaying it in the UI

  • Implementing actions based on the detected text

  • Handling multiple lines or paragraphs of text

Enhancing Text Detection Accuracy

Several methods and approaches can be used to enhance text detection's precision. In this section, we delve into the best practices for fine-tuning text recognition, utilizing image preparation tools, and adapting to varying lighting and viewpoints.

Adjusting Text Recognition Settings

  • Configuring the TextRecognizer for different scenarios

  • Setting the desired text detection accuracy level

  • Fine-tuning recognition parameters for specific requirements

Implementing Image Preprocessing Techniques

  • Applying image filters and enhancements for better text visibility

  • Removing noise, blur, and artifacts from the image

  • Enhancing contrast and sharpness of the image

Taking Care of Different Lighting Situations And Angles

  • Getting used to different kinds of light and shadows

  • Text orientation and viewpoint can be hard to deal with.

  • Using formulas to fix text that is crooked or out of shape

Customizing Text Detection UI

The user interface is one of the most important parts of text recognition. This part is about designing the camera preview interface, putting text detection results on top of the camera preview, and adding interactive features for text selection.

Designing The Camera Preview Interface

  • Creating a custom camera preview layout

  • Adding camera controls and capture buttons

  • Optimizing the UI for different screen sizes and orientations

Putting the Results of Text Recognition on Top of The Camera Preview

  • The camera preview shows text border boxes and notes.

  • Formatting and highlighting found text to make it easier to see

  • Real-time changes as new text is found

Adding Tools That Let You Interact with Text Selection

  • Making it possible to pick text with a touch or gesture

  • Doing things with the words you've chosen, like copy or translate

  • Making the user experience better with easy-to-use interfaces

Handling Text Recognition Errors

It is possible for mistakes to be made during the text recognition process. Strategies for coping with low-confidence results, incorporating error handling systems, and providing user feedback for unsuccessful detections are discussed in this section.

Dealing With Low-Confidence Results

  • Establishing confidence thresholds for text recognition

  • Handling cases of uncertain or ambiguous text recognition

  • Implementing fallback mechanisms for low-confidence results

Implementing Error Handling Mechanisms

  • Detecting and handling exceptions during text recognition

  • Logging and reporting errors for debugging purposes

  • Providing fallback options when text detection fails

Giving Users Feedback When a Scan Fails

  • Letting the user know when text recognition fails.

  • Giving ideas or instructions on how to improve results.

  • Building in ways for users to give comments and report errors

Optimizing Performance and Efficiency

Efficiency and effectiveness are fundamental to the success of any program. Real-time processing, better use of system resources, longer battery life, and lower latency are all goals of this section's optimization work on text detection.

Implementing Real-Time Text Detection

  • Utilizing threading and parallel processing for faster recognition

  • Optimizing image capture and processing pipelines

  • Implementing techniques for real-time text display and updates

Managing System Resources Effectively

  • Monitoring memory usage and optimizing resource allocation

  • Implementing strategies for efficient CPU and GPU utilization

  • Minimizing unnecessary computations and memory leaks

Improving Battery Life and Reducing Latency

  • Implementing power-saving measures during image capture and processing

  • Reducing network usage and optimizing data transfer

  • Implementing caching mechanisms to minimize redundant operations

Testing and Debugging

For text detection to always produce accurate results, it must be thoroughly tested and debugged. Unit testing text detection capabilities, analyzing and troubleshooting typical errors, and contrasting emulator and real device testing are all discussed in this section.

Performing Unit Tests for Text Detection Functionality

  • Implementing test cases to validate the text detection algorithms

  • Verifying the accuracy and performance of the text recognition

Analyzing and Troubleshooting Common Issues

  • Identifying and resolving common errors or bugs in text detection

  • Analyzing logs, error messages, and debugging information

  • Troubleshooting issues related to image quality, lighting, or environment

Emulator Vs. Real Device Testing

  • Comparing text detection performance on emulator and real devices

  • Addressing device-specific challenges and limitations

  • Gathering feedback from different devices and Android versions


In conclusion, developers have limitless options once they learn how to build a text detector in Android with Firebase ML Kit. Applications can provide better user experiences, expedite document processing, and enable new functionality with accurate and efficient text recognition. This guide will show you how to leverage text detection in your Android app to its full potential, allowing you to provide users with more engaging and insightful interactions.

Someswar Pal
Someswar Pal

Studying Mtech/ AI- ML

Updated on: 05-Oct-2023


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