Digit Recognition using MNIST – IT and Computer Engineering Guide
1. Project Overview
Objective: Recognize handwritten digits using the MNIST
dataset.
Scope: Use Convolutional Neural Networks (CNNs) or traditional machine learning
algorithms for digit classification.
2. Prerequisites
Knowledge: Basics of Python programming, neural networks,
and image preprocessing.
Tools: Python, TensorFlow/Keras, Scikit-learn, NumPy, Matplotlib, Pandas.
Dataset: MNIST dataset available in TensorFlow or other public repositories.
3. Project Workflow
- Data Collection: Load the MNIST dataset from TensorFlow or download it manually.
- Data Preprocessing: Normalize pixel values and reshape images for input to models.
- Exploratory Data Analysis (EDA): Visualize sample digits and check data distribution.
- Model Development: Build models using CNNs or traditional machine learning techniques like SVM or KNN.
- Model Evaluation: Use metrics such as accuracy, precision, recall, and confusion matrix.
- Optimization: Fine-tune model hyperparameters and explore different architectures.
- Deployment: Deploy the trained model as a web service or application.
4. Technical Implementation
Step 1: Import Libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
from sklearn.metrics import classification_report, confusion_matrix
Step 2: Load and Preprocess the Dataset
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# Normalize pixel values
X_train = X_train / 255.0
X_test = X_test / 255.0
# Reshape for CNN input
X_train = X_train.reshape(-1, 28, 28, 1)
X_test = X_test.reshape(-1, 28, 28, 1)
Step 3: Build a CNN Model
model = Sequential([
Conv2D(32, (3, 3), activation='relu',
input_shape=(28, 28, 1)),
MaxPooling2D((2, 2)),
Flatten(),
Dense(128, activation='relu'),
Dense(10, activation='softmax')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
Step 4: Train the Model
model.fit(X_train, y_train, epochs=5, validation_split=0.2)
Step 5: Evaluate the Model
loss, accuracy = model.evaluate(X_test, y_test)
print(f"Test Accuracy: {accuracy}")
# Predictions and Metrics
y_pred = np.argmax(model.predict(X_test), axis=1)
print(classification_report(y_test, y_pred))
5. Results and Visualization
Visualize the confusion matrix and analyze misclassified
digits.
Plot sample predictions to demonstrate model performance.
6. Challenges and Mitigation
Overfitting: Use dropout layers and regularization.
Class imbalance: Ensure dataset is balanced or use weighted loss functions.
7. Future Enhancements
Extend the model to handle other handwritten datasets like
EMNIST.
Deploy the model in a mobile or web application for real-time predictions.
8. Conclusion
The Digit Recognition project demonstrates the power of CNNs
in image classification.
It provides insights into the workflow of building and optimizing machine
learning models.