MyanNet-V1

MyanNet V1

<p align="center"> <img src="figures/banner.png" width="100%"> </p> <p align="center">

A Lightweight CNN for Burmese Handwritten Digit Recognition

TensorFlow • Keras • TensorFlow Lite • Edge AI

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Overview

MyanNet V1 is a lightweight convolutional neural network (CNN) developed for Burmese handwritten digit recognition. The model was designed to achieve an excellent balance between recognition accuracy and computational efficiency, making it suitable for deployment on mobile, embedded, and resource-constrained devices.

Instead of maximizing accuracy through increasingly deeper networks, MyanNet V1 focuses on reducing computational complexity while maintaining competitive performance. The architecture combines depthwise separable convolutions, batch normalization, global average pooling, and dropout regularization to produce a compact yet highly effective classifier.

The model was trained and evaluated on the Burmese Handwritten Digit Dataset (BHDD), containing 87,561 handwritten digit images across ten Burmese numeral classes.

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Model Highlights

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Architecture

MyanNet V1 follows a compact two-stage convolutional architecture optimized for lightweight deployment.

Feature Extractor

• Standard Convolution
• Batch Normalization
• Max Pooling
• Depthwise Separable Convolution
• Batch Normalization
• Max Pooling

Classification Head

• Global Average Pooling
• Dropout
• Fully Connected Layer
• Softmax Output

The architecture significantly reduces parameter count while preserving classification accuracy.

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Performance Comparison

MyanNet V1 achieves competitive accuracy while reducing the number of trainable parameters by approximately 69.5% compared to the baseline CNN.

<p align="center"> <img src="figures/model_comparison.png" width="800"> </p>

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Training Curves

The training and validation curves demonstrate stable convergence with minimal overfitting.

<p align="center"> <img src="figures/training_curves.png" width="800"> </p>

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Confusion Matrix

The confusion matrix shows strong classification performance across all Burmese digit classes.

<p align="center"> <img src="figures/confusion_matrix.png" width="700"> </p>

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5-Fold Cross Validation

The model was evaluated using stratified five-fold cross validation to assess robustness and generalization.

<p align="center"> <img src="figures/kfold_results.png" width="700"> </p>

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Sample Predictions

Example handwritten digit samples from the BHDD dataset.

<p align="center"> <img src="figures/sample_images.png" width="800"> </p>

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Failure Cases

Representative misclassified samples produced by MyanNet V1.

<p align="center"> <img src="figures/misclassified_samples.png" width="800"> </p>

Although misclassifications are rare, they primarily occur for ambiguous handwriting styles and visually similar digit shapes.

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Dataset

The model was trained and evaluated on the Burmese Handwritten Digit Dataset (BHDD).

Dataset Summary:

• 87,561 handwritten digit images
• 10 Burmese digit classes (၀–၉)
• 28 × 28 grayscale images
• MNIST-compatible format

Training Split:

• 60,000 balanced training images

Testing Split:

• 27,561 naturally imbalanced testing images

Please obtain the dataset from the official BHDD repository.

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Usage

TensorFlow / Keras

import tensorflow as tf

model = tf.keras.models.load_model("myannet_best.keras")

prediction = model.predict(image)

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TensorFlow Lite

import tensorflow as tf
import numpy as np

interpreter = tf.lite.Interpreter(
    model_path="myannet_quantized.tflite"
)

interpreter.allocate_tensors()

input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

image = np.expand_dims(image / 255.0, axis=(0, -1)).astype(np.float32)

interpreter.set_tensor(input_details[0]["index"], image)

interpreter.invoke()

prediction = interpreter.get_tensor(output_details[0]["index"])

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Intended Applications

MyanNet V1 is suitable for:

• Burmese handwriting recognition
• Educational software
• Mobile OCR systems
• Embedded AI devices
• Edge computing
• Research benchmarking

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Limitations

MyanNet V1 was trained exclusively on isolated handwritten Burmese digits contained in the BHDD dataset.

The model has not been evaluated on:

• Printed Burmese text
• Burmese characters
• Burmese words or sentences
• Historical manuscripts
• Camera-captured documents
• General OCR tasks

Performance outside the BHDD domain may differ significantly.

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Version Information

This repository contains the original public release of MyanNet (Version 1).

Version 1 established the lightweight CNN architecture and serves as the baseline for future iterations.

Future versions aim to improve:

• Model compactness
• Computational efficiency
• Robustness
• Recognition performance
• Support for larger Burmese OCR tasks

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Citation

If you use MyanNet V1 in your research, please cite:

@software{maung2026myannetv1,
  author    = {Ah Maung Oo},
  title     = {MyanNet V1: A Lightweight CNN for Burmese Handwritten Digit Recognition},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/Drew2456/MyanNet-V1}
}

Once the associated journal paper is published, this citation will be updated with the official publication.

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Acknowledgements

• BHDD dataset by Swan Htet Aung et al.
• TensorFlow & Keras
• Optuna for hyperparameter optimization
• The open-source machine learning community

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⭐ If you find MyanNet V1 useful, please consider starring the GitHub repository and citing this work in your research.