Handwritten Character Recognition Classifier

Introduction

In this project, you will learn how to build a simple handwritten character recognition classifier using the DIGITS dataset provided by the scikit-learn library. Handwritten character recognition is a classic problem in machine learning, and this project will guide you through the process of creating a classifier that can accurately predict the digit represented in a handwritten character image.

🎯 Tasks

In this project, you will learn:

How to load the DIGITS dataset and split it into training and testing sets
How to create and train a Support Vector Machine (SVM) classifier on the training data
How to implement a function to classify a single handwritten character image
How to test the classifier with a sample handwritten character image

🏆 Achievements

After completing this project, you will be able to:

Load and preprocess a dataset for machine learning tasks
Create and train an SVM classifier using scikit-learn
Implement a prediction function to classify new samples
Understand the basics of handwritten character recognition using machine learning techniques

Load the Digits Dataset

In this step, you will learn how to load the DIGITS dataset from the scikit-learn library. Follow the steps below to complete this step:

Open the handwritten_digit_classifier.py file, import the necessary libraries:

from sklearn import datasets
from sklearn.model_selection import train_test_split

Load the DIGITS dataset using the datasets.load_digits() function:

digits = datasets.load_digits()
X, y = digits.data, digits.target

The X variable contains the flattened 8x8 pixel images, and the y variable contains the corresponding digit labels (0-9).

Split the dataset into training and testing sets using train_test_split():

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

This will split the data into 80% training and 20% testing sets.

Create and Train the SVM Classifier

In this step, you will learn how to create and train a Support Vector Machine (SVM) classifier on the training data. Follow the steps below to complete this step:

Import the SVC class from the sklearn.svm module in the handwritten_digit_classifier.py file:

from sklearn.svm import SVC

Create an SVM classifier with a linear kernel and a regularization parameter of 1:

clf = SVC(kernel="linear", C=1)

Train the SVM classifier on the training data using the fit() method:

clf.fit(X_train, y_train)

This will train the SVM classifier on the training data.

Implement the Prediction Function

In this step, you will implement the predict(sample) function to classify a single handwritten character image. Follow the steps below to complete this step:

Import the numpy module in the handwritten_digit_classifier.py file:

import numpy as np

Define the predict(sample) function:

def predict(sample):
    """
    Parameters:
    sample -- A list of pixel values of a handwritten character image

    Returns:
    pred -- The predicted label for the handwritten character image as an integer
    """
    ## Reshape the input sample
    sample = np.array(sample).reshape(1, -1)

    ## Use the trained classifier to make a prediction
    pred = clf.predict(sample)

    return int(pred[0])

In the predict(sample) function:

Convert the input sample list into a NumPy array and reshape it to have a single sample with the same format as the training data.
Use the trained clf classifier to predict the label for the reshaped input sample using the predict() method.
Return the predicted label as an integer.

Test the Classifier

You can now test the predict(sample) function with a sample handwritten character image. Here's an example in the handwritten_digit_classifier.py file:

sample = [
    0.0, 0.0, 6.0, 14.0, 4.0, 0.0, 0.0, 0.0,
    0.0, 0.0, 11.0, 16.0, 10.0, 0.0, 0.0, 0.0,
    0.0, 0.0, 8.0, 14.0, 16.0, 2.0, 0.0, 0.0,
    0.0, 0.0, 1.0, 12.0, 12.0, 11.0, 0.0, 0.0,
    0.0, 0.0, 0.0, 0.0, 11.0, 3.0, 0.0, 0.0,
    0.0, 0.0, 0.0, 0.0, 5.0, 11.0, 0.0, 0.0,
    0.0, 1.0, 4.0, 4.0, 7.0, 16.0, 2.0, 0.0,
    0.0, 7.0, 16.0, 16.0, 13.0, 11.0, 1.0, 0.0
]

result = predict(sample)
print("Predicted Label:", result)

This should output the predicted label for the given handwritten character image.

Run the handwritten_digit_classifier.py file to execute the example:

python handwritten_digit_classifier.py
## Predicted Label: 9

Summary

Congratulations! You have completed this project. You can practice more labs in LabEx to improve your skills.

Simple Handwritten Character Recognition Classifier