README.md

---
language:
- en
license: apache-2.0
tags:
- image-classification
- computer-vision
- digit-recognition
- mnist
- tensorflow
- keras
pipeline_tag: image-classification
metrics:
- accuracy
---

# Digit Recognition Model

## Model Summary

A convolutional neural network (CNN) trained to classify handwritten digits (0–9) from image input. Trained on the MNIST dataset, this model serves as a foundational computer vision project demonstrating image classification with deep learning.

---

## Model Details

- **Developed by:** Chandrasekar Adhithya Pasumarthi ([@Adhithpasu](https://github.com/Adhithpasu))
- **Affiliation:** Frisco ISD, TX | AI Club Leader | Class of 2027
- **Model type:** Convolutional Neural Network (CNN)
- **Framework:** TensorFlow / Keras
- **License:** Apache 2.0
- **Related work:** Part of a broader ML/CV portfolio including research on Vision Transformers vs CNNs — *JCSTS Vol. 8(2), January 2026*

---

## Intended Uses

**Direct use:**
- Handwritten digit classification (0–9)
- Educational demonstrations of CNNs and image classification pipelines
- Baseline model for comparing against more advanced architectures (ViT, ResNet, etc.)

**Out-of-scope use:**
- Multi-character or multi-digit recognition (e.g., full number strings)
- Non-MNIST-style digit distributions without fine-tuning

---

## Training Data

Trained on the **MNIST dataset** — 60,000 training images and 10,000 test images of 28×28 grayscale handwritten digits.

---

## Evaluation

| Metric   | Value |
|----------|-------|
| Accuracy | TBD   |
| Loss     | TBD   |

*(Fill in with your actual test set results)*

---

## How to Use

```python
import tensorflow as tf
import numpy as np
from PIL import Image

# Load model
model = tf.keras.models.load_model("digit_recognition_model")

# Load and preprocess a 28x28 grayscale image
img = Image.open("digit.png").convert("L").resize((28, 28))
img_array = np.array(img) / 255.0
img_array = img_array.reshape(1, 28, 28, 1)

# Predict
prediction = model.predict(img_array)
print(f"Predicted digit: {np.argmax(prediction)}")
```

---

## Model Architecture

```
Input (28x28x1)
→ Conv2D(32, 3x3, relu) → MaxPooling2D
→ Conv2D(64, 3x3, relu) → MaxPooling2D
→ Flatten
→ Dense(128, relu) → Dropout(0.5)
→ Dense(10, softmax)
```

*(Update to match your actual architecture)*

---

## Limitations & Bias

- Performs best on MNIST-style centered, normalized digits
- May degrade on real-world handwriting without preprocessing or fine-tuning
- Limited to single digit classification

---

## Citation

```bibtex
@misc{pasumarthi2026digitrecognition,
  author    = {Chandrasekar Adhithya Pasumarthi},
  title     = {Digit Recognition Model},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/Chandrasekar123/DigitRecognition}
}
```

---

## Contact

- GitHub: [@Adhithpasu](https://github.com/Adhithpasu)