README.md

---
language:
- my

license: apache-2.0

library_name: tensorflow

pipeline_tag: image-classification

tags:
- burmese
- myanmar
- handwritten
- digit-recognition
- computer-vision
- cnn
- tensorflow
- keras
- tflite
- lightweight
- edge-ai

datasets:
- expa-ai/BHDD
---

# 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

</p>

---

## 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.

---

# Model Highlights

| Property | Value |
|-----------|-------|
| **Task** | Burmese Handwritten Digit Recognition |
| **Framework** | TensorFlow / Keras |
| **Input Size** | 28 × 28 Grayscale |
| **Classes** | 10 (၀–၉) |
| **Trainable Parameters** | **10,634** |
| **Test Accuracy** | **99.49%** |
| **5-Fold CV Accuracy** | **99.46% ± 0.06%** |
| **Quantized Model Size** | **24.18 KB** |
| **Average CPU Inference** | **0.263 ms/image** |
| **License** | Apache-2.0 |

---

# 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.

---

# 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>

| Model | Parameters | Accuracy |
|---------|-----------:|----------:|
| Baseline CNN | 34,826 | 99.58% |
| GAP-BN CNN | 21,418 | 99.51% |
| **MyanNet V1** | **10,634** | **99.49%** |

---

# 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>

---

# 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>

---

# 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>

| Metric | Value |
|---------|------|
| Mean Accuracy | **99.46%** |
| Standard Deviation | **0.06%** |

---

# Sample Predictions

Example handwritten digit samples from the BHDD dataset.

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

---

# 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.

---

# 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.

---

# Usage

## TensorFlow / Keras

```python
import tensorflow as tf

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

prediction = model.predict(image)
```

---

## TensorFlow Lite

```python
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"])
```

---

# Intended Applications

MyanNet V1 is suitable for:

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

---

# 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.

---

# 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

---

# Citation

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

```bibtex
@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.

---

# Acknowledgements

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

---

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