🖊 Handwritten Digit Recognition (HDR) Project

📜 Project Description

The Digit Recognition project aims to create a system capable of accurately identifying handwritten digits. The process involves:

• 📥 Collecting a dataset of handwritten digit images, such as the MNIST dataset, labeled with their corresponding digits.
• 🛠️ Applying preprocessing techniques like normalization and resizing to standardize the images.
• 🔍 Extracting features from the images using techniques such as pixel intensities, Histogram of Oriented Gradients (HOG), and edge detection.
• 🧠 Training machine learning models, such as Support Vector Machines (SVM), Random Forests, or deep learning architectures like Convolutional Neural Networks (CNNs), on the extracted features.
• 📈 Evaluating the model’s performance using metrics like accuracy and confusion matrix.
• 🔧 Fine-tuning the model and optimizing hyperparameters to improve its accuracy.
• 🚀 Deploying the trained model into a real-world application that can accurately recognize handwritten digits in real-time.

⚙️ Features

1. Pixel Intensities:

• 🎨 The most straightforward feature is the intensity value of each pixel in the image.
• ⚫ Each pixel’s intensity serves as a feature, with grayscale images having values ranging from 0 (black) to 255 (white).

2. HOG (Histogram of Oriented Gradients):

• 🧭 A feature descriptor used to capture shape information in an image.
• 🌀 Computes the distribution of gradient orientations in localized portions of the image.

3. Edge Detection:

• 🖼️ Features are derived from detected edges within the image using techniques like Sobel, Canny, or Prewitt edge detectors.

4. Corner Detection:

• 🧩 Features can be extracted from corner points in the image using algorithms like the Harris corner detector.

5. Texture Features:

• 🧵 Texture information is captured using techniques such as co-occurrence matrices or local binary patterns (LBP).

6. Zernike Moments:

• 🔮 Zernike moments are orthogonal moments used to capture shape information, especially effective for binary images.

📊 Model Performance Evaluation

• ✔️ Accuracy: Measures the percentage of correctly classified digits.
• 🔀 Confusion Matrix: Evaluates model performance by comparing true labels with predicted labels.

🚀 Deployment

After training and fine-tuning, the model is deployed for real-time handwritten digit recognition.

💻 Technologies Used

• 🐍 Python
• 📊 Machine Learning (SVM, Random Forest, CNN)
• 📦 Libraries: Scikit-learn, TensorFlow/Keras, OpenCV

📚 Dataset

MNIST: A large dataset of handwritten digits commonly used for training various image processing systems.

📂 Project Demo Link:

• Youtube: https://youtu.be/GUK9RGgqgwo
• Medium Blog: https://thesupriyanagpal.medium.com/building-a-handwritten-digit-recognition-system-a-comprehensive-guide-184ce239469f
• Colab: https://colab.research.google.com/drive/1q4ZK4H-LBkFzbs1nUh-1w-wBL7p_Jubv?usp=sharing

🤝 Social Links

• LinkedIn: https://www.linkedin.com/in/supriyanagpal
• Medium: https://medium.com/@thesupriyanagpal
• Twitter: https://x.com/imsupriyanagpal
• Youtube: https://www.youtube.com/@supriyanagpal