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README.md

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 ---
+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/AndrewMaung/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.