✍️ Handwritten Character Recognition using Deep Learning

CodeAlpha Internship Task 2

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📌 Overview

This project focuses on recognizing handwritten digits/characters using deep learning techniques. It is developed as part of the CodeAlpha Machine Learning Internship and demonstrates image classification using neural networks.

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🚀 Features

• 🧠 Deep Learning-based classification
• 🖼️ Image processing and feature extraction
• 🔢 Handwritten digit recognition (0–9)
• 📊 Model training and evaluation
• 📉 Accuracy measurement and prediction

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🛠️ Tech Stack

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📊 Dataset

The project uses a standard handwritten digits dataset (such as MNIST/digits dataset), which contains images of digits from 0 to 9 used for training and testing the model.

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📁 Project Structure

├── notebooks/
│   └── handwritten_character_recognition.ipynb
├── images/
│   ├── sample images.png
│   └── class distribution.png
│   ├── cnn_training_history.png
│   ├── cnn_confusion_matrix.png
│   └── cnn_predictions.png
├── requirements.txt
└── README.md

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📈 Model Workflow

1. Load dataset
2. Preprocess image data
3. Split into training and testing sets
4. Train model (MLP / Neural Network)
5. Evaluate performance
6. Predict handwritten digits

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📊 Data Analysis

🔹 Sample Images

The dataset contains handwritten digit images used for training and testing.

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🔹 Class Distribution

The dataset shows a balanced distribution across all digit classes.

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🤖 Model Training (CNN)

🔹 Training History

The model shows steady improvement in accuracy and reduction in loss over epochs.

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📉 Model Evaluation

🔹 Confusion Matrix

The confusion matrix indicates strong classification performance with minimal misclassification.

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🔍 Predictions

🔹 CNN Predictions on Test Samples

The model successfully predicts handwritten digits on unseen test data.

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📈 Results

• The model successfully classifies handwritten digits
• Achieved good accuracy on test data
• Demonstrates effectiveness of neural networks for image classification

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⚙️ How to Run

🔹 Step 1: Clone Repository

git clone https://github.com/Rosesharma13/CodeAlpha_HandwrittenRecognition.git
cd CodeAlpha_HandwrittenRecognition

🔹 Step 2: Install Dependencies

pip install -r requirements.txt

🔹 Step 3: Run the Project

• Open the notebook in Jupyter Notebook or Google Colab
• Run all cells

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🔮 Future Improvements

• Improve accuracy using advanced CNN architectures
• Extend to handwritten word recognition
• Deploy model as a web application
• Add real-time image input

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🙌 Acknowledgements

• Dataset: MNIST
• Developed as part of CodeAlpha Internship

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👩‍💻 Author

Rose Sharma | CodeAlpha ML Internship