Master Handwriting Recognition with Google ML Kit

Home AI News Master Handwriting Recognition with Google ML Kit

Master Handwriting Recognition with Google ML Kit

Introduction
Steps to Create an App
Handwriting Recognition Tutorial
Improving Handwriting Recognition
- Best Practices
- Final Processing Step
Conclusion
FAQ

Introduction

Welcome to the baqwas Channel! In this Tutorial, we will explore the fascinating world of natural language processing for Handwriting recognition using an Android device. Our objective is to use a handheld device to recognize handwriting and related drawings. This tutorial is derived from Chapter 5 of Laurence Moroney's latest book, with some modifications to the code to incorporate the latest supported versions of all dependencies. Prior familiarity with Android Studio will be helpful in following along with the video demonstration.

Steps to Create an App

Create a Project

To begin, we need to create a new project in Android Studio. Launch Android Studio and navigate to File in the menu bar. Select New from the drop-down menu and choose the Empty Activity template. Enter the preferred name, location, and language for the app. Make sure to define all variables and dependencies in the appropriate build.gradle files.

Create a User Interface

Next, we will create a user interface for our app. In the project folder view, navigate to app > res > layout and open activity_main.xml. Here, we will insert three control fields under a LinearLayout: DrawingSurface for entering the handwriting, TextView for displaying the results, and another LinearLayout for running the application and clearing any unnecessary handwriting.

Code the Processing Functions

Now, we will code the processing functions for our handwriting recognition app. We will use the Canvas object to draw the user's writings on the screen. The TouchEvent interrupt handles user interactions with the drawing surface. We will implement three methods: touchStart, touchMove, and touchUp. These methods will capture the strokes made by the user on the drawing surface.

Integrate the Code

Once the processing functions are implemented, we need to integrate the code into our app. This involves creating a recognizer object with the model and passing the ink (i.e., strokes collection) to the model for recognition. The results returned by the recognizer will consist of the recognized text and their corresponding ink.

Run the App

Finally, it's time to run our app. Build the project and make sure the build is successful. You can now launch the app on any supported Android device. Follow the instructions provided in the app to capture handwritten input text, classify the handwriting, and clear the drawing surface. The results of the recognition will be displayed in the app.

Handwriting Recognition Tutorial

Setting Up the Project

To begin the handwriting recognition tutorial, we first need to set up the project in Android Studio. Launch Android Studio and create a new project by selecting the Empty Activity template. Make sure to update the build.gradle files with the required dependencies and variables.

Creating the User Interface

Next, we will create the user interface for our handwriting recognition app. Open the activity_main.xml file and add three control fields under a LinearLayout: DrawingSurface for the user to write or draw, TextView for displaying the recognition results, and a horizontal layout with three buttons for running the recognition, clearing the drawing surface, and selecting the language for handwriting recognition.

Processing the Handwriting

In this step, we will process the user's strokes into text using the ML Kit. We will initialize the model with the desired language and locale combinations, build a reference to the model, download the model if necessary, create a recognizer object with the model, and pass the ink (i.e., collection of strokes) to the model for recognition. The ML Kit supports over 300 languages and 25+ writing systems and can even recognize emoji symbols and basic HAND-drawn shapes.

Displaying the Results

Once the recognition is complete, we need to display the results to the user. Iterate through the collection of recognition results, and for each result, display the recognized text along with the corresponding ink. The accuracy of the classification depends on factors such as handwriting style, stroke ordering, and Shape classifiers.

Improving Handwriting Recognition

Best Practices

To improve the accuracy of handwriting text recognition, there are a few best practices to follow. First, provide the Dimensions of the writing area to the recognition process. Pre-context can also help resolve ambiguity in recognition. Taking stroke ordering into account can significantly improve recognition accuracy. Additionally, shape classifiers can provide scores to further refine the recognition results.

Final Processing Step

In the final processing step, we iterate through the collection of results and extract the recognized text and corresponding ink. These results can then be further processed or presented to the user in a suitable format. ML Kit offers the flexibility to extend the recognition capabilities of the app and supports various writing systems, including all major Latin languages, Chinese, Japanese, Korean, Arabic, and Cyrillic.

Conclusion

In this tutorial, we explored the introductory use of natural language processing for handwriting recognition using an Android device. We learned how to create an app from scratch, create a user interface, code the processing functions, integrate the code, and display the recognition results. By following best practices and using ML Kit's powerful recognition capabilities, we can improve the accuracy of handwriting recognition in our app.

FAQ

Q: Can this app recognize handwriting in multiple languages?
A: Yes, ML Kit supports over 300 languages and 25+ writing systems, including all major Latin languages, Chinese, Japanese, Korean, Arabic, and Cyrillic. Please refer to the complete list of supported languages for more information.

Q: How does this app handle hand-drawn shapes and emoji symbols?
A: The app can recognize basic hand-drawn shapes and emoji symbols using ML Kit's recognition capabilities. This adds versatility to the app and enhances its recognition capabilities.

Q: Can I extend the functionality of this app beyond handwriting recognition?
A: Yes, this app serves as a foundation for more advanced applications of natural language processing. By building upon the existing code and leveraging ML Kit's features, you can create apps that extract contextual information from text input and explore various other use cases.

Q: What are the key factors that affect the accuracy of handwriting recognition?
A: The accuracy of handwriting recognition depends on various factors, including the style of handwriting, stroke ordering, and the presence of pre-context. Following best practices and considering these factors can significantly improve the accuracy of recognition.

Q: Are there any open-source tools used in the development of this app?
A: Yes, this app was developed using various open-source tools, including Kdenlive for non-linear editing, LibreOffice for documentation, OBS Studio for recordings, and the Ubuntu platform for services. These tools have contributed to the creation of this tutorial.

Resources: