Add"Handwritten digit prediction model using Mniset data" under Machine learning section

[0-Shimanshu]

🔢 Project Contribution: Handwritten_Digit_Prediction_Program I would like to contribute my Machine Learning + Web App project titled "Handwritten_Digit_Prediction_Program" under the Deep_Learning, Digit_Recognition, or Web_Integrated_AI section of this repository as part of GirlScript Summer of Code 2025 (GSSoC'25).

📌 Project Overview: Handwritten_Digit_Prediction_Program is a deep learning-based project designed to classify handwritten digits (0–9) using a Multilayer Perceptron (MLP). The model is trained on the MNIST dataset, which contains thousands of handwritten digit images, and is capable of predicting digits in real time via a user-friendly web interface. This project demonstrates the application of neural networks for image classification, even without convolutional layers, using only fully connected dense layers and pixel data.

🧠 Model Overview: Framework: Python with TensorFlow/Keras, NumPy, and scikit-learn Architecture: Multilayer Perceptron (Fully Connected Layers) Input: 28x28 grayscale images flattened to 1D vectors Preprocessing: Normalization, grayscale conversion, reshaping Output: 10-class softmax (digits 0 to 9) Optimization: Backpropagation with categorical crossentropy loss Evaluation: Accuracy, precision, recall Confusion matrix for detailed error analysis

🧰 Features: 🔁 Trained models saved in .h5 and .keras formats 📁 MNIST data handled from .idx format 🧠 Clean preprocessing and training scripts 🌐 Flask-powered backend (app.py) 🖥️ HTML + CSS + JavaScript frontend for UI 🎯 Real-time image upload and prediction output 📈 Performance evaluated using multiple classification metrics

📦 Handwritten_Digit_Prediction_Program/ ├── DATABASE/ # Raw MNIST dataset (IDX format) ├── model/ # Trained MLP models (.h5, .keras) ├── static/ # Frontend styling and scripts │ ├── style.css │ └── script.js ├── templates/ │ └── webpage.html # User input and prediction display ├── app.py # Flask app for backend prediction ├── .ipynb_checkpoints/ # Jupyter notebook backups ├── .idea/ # IDE-specific configs └── README.md # (Can be improved for instructions)

✅ Deliverables: ✔️ Modular, well-documented Python code ✔️ Trained digit recognition model (MLP) ✔️ Flask web app for real-time predictions ✔️ Frontend interface to upload images and display results ✔️ Performance metrics including confusion matrix ✔️ Inference-ready .h5/.keras model files

👨‍💻 Full Name: Shimanshu Chouhan

🎓 Participant Role: GSSoC'25 Contributor

[github-actions[bot]]

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[kashishhMehra]

Hi sir, i wanted to ask you some questions regarding this issue and contribute as well, I cant find you on the GSSoC server.

[UTSAVS26]

@kashishhMehra i have made a post recently in the project forum you can check there and find my id.

[kashishhMehra]

Okay sir

[kashishhMehra]

Hi sir, sorry i tried but i cannot find your name