If you are looking to merge the worlds of programming, artificial intelligence, and mobile development, creating a handwritten Korean character recognition app is a fascinating project. This guide will walk you through the steps of building an Android application that uses a TensorFlow model trained to recognize Korean syllables. Let’s get started!

Understanding the Concept

Imagine teaching a child how to recognize letters. You show them flashcards with different characters and repeat the names until they learn to identify them. In our project, we will go through a similar process using synthetic images of Hangul, the Korean alphabet, training a model to recognize these drawings made by users on their devices.

Prerequisites

• An Android Studio setup
• Basic knowledge of Python and TensorFlow
• A basic understanding of the Android app development lifecycle

Steps to Follow

1. Clone the Repository

Start by cloning the TensorFlow Hangul Recognition repository. Open your terminal and run:

git clone https://github.com/IBM/tensorflow-hangul-recognition

Then navigate to the cloned directory:

cd tensorflow-hangul-recognition

2. Install Dependencies

It’s recommended to use a virtual environment for Python development. Depending on your Python version, set it up with:

$ python -m venv mytestenv  # Python 3.X
$ virtualenv mytestenv         # Python 2.X
$ source mytestenv/bin/activate   # For Mac or Linux
$ mytestenv\Scripts\activate        # For Windows PowerShell

Then install necessary Python packages:

pip install -r requirements.txt

3. Generate Image Data

Since we need a dataset of handwritten characters, we will generate images using Korean fonts. A provided script can help create the dataset using various font files.

python .tools/hangul-image-generator.py --label-file your_label_file_path

After running it, you will have a folder filled with images of Korean characters ready for training.

4. Convert Images to TFRecords

Next, convert your images into TFRecords format, which TensorFlow prefers for data input:

python .tools/convert-to-tfrecords.py --label-file your_label_file_path

5. Train the Model

Now it’s time to train your TensorFlow model to recognize the characters:

python .hangul_model.py --label-file your_label_file_path

This step may take some time, and having a GPU would significantly speed up training. Patience is key here!

6. Test the Model

Before building the Android application, test your model with sample images:

python .tools/classify-hangul.py Image_Path --label-file your_label_file_path

7. Create the Android Application

With a properly trained model, set up your Android app in Android Studio. Open the project and follow through with the following:

1. Import the TensorFlow model to your application.
2. Use Java classes provided to facilitate drawing and recognition.
3. Link the app to the Watson Language Translator API to add translation capabilities.

Troubleshooting

If you encounter issues during any of these steps, here are some troubleshooting tips:

• Ensure that paths are correctly set for the images and TensorFlow model.
• Be sure that all required dependencies are installed.
• If you have issues with model outputs, check the dimension of your input images to be 64×64 pixels.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

Creating a handwritten Korean character recognition app is a rewarding project that combines various technologies and skills. Remember to iterate, learn from errors, and celebrate each small victory along the way. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.