---
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
**A Lightweight CNN for Burmese Handwritten Digit Recognition**
TensorFlow • Keras • TensorFlow Lite • Edge AI
---
## 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.
| 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.
---
# Confusion Matrix
The confusion matrix shows strong classification performance across all Burmese digit classes.
---
# 5-Fold Cross Validation
The model was evaluated using stratified five-fold cross validation to assess robustness and generalization.
| Metric | Value |
|---------|------|
| Mean Accuracy | **99.46%** |
| Standard Deviation | **0.06%** |
---
# Sample Predictions
Example handwritten digit samples from the BHDD dataset.
---
# Failure Cases
Representative misclassified samples produced by MyanNet V1.
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.