Recognizing hand-written digits

This example shows how scikit-learn can be used to recognize images of hand-written digits, from 0-9.

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

# Standard scientific Python imports
import matplotlib.pyplot as plt

# Import datasets, classifiers and performance metrics
from sklearn import datasets, metrics, svm
from sklearn.model_selection import train_test_split

Digits dataset

The digits dataset consists of 8x8 pixel images of digits. The images attribute of the dataset stores 8x8 arrays of grayscale values for each image. We will use these arrays to visualize the first 4 images. The target attribute of the dataset stores the digit each image represents and this is included in the title of the 4 plots below.

Note: if we were working from image files (e.g., ‘png’ files), we would load them using matplotlib.pyplot.imread.

digits = datasets.load_digits()

_, axes = plt.subplots(nrows=1, ncols=4, figsize=(10, 3))
for ax, image, label in zip(axes, digits.images, digits.target):
    ax.set_axis_off()
    ax.imshow(image, cmap=plt.cm.gray_r, interpolation="nearest")
    ax.set_title("Training: %i" % label)