Update README.md

README.md

@@ -1,8 +1,127 @@
 ---
-license: mit
 language:
 - en
-pipeline_tag: image-classification
+license: apache-2.0
 tags:
-- code
----
+- 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)