Recognizing Handwritten Digits with Scikit-Learn

Recognizing Handwritten Digits with Scikit-Learn

This article presents recognizing the handwritten digits (0 to 9) using the famous digits data set from Scikit-Learn, using a classifier called Logistic Regression.

Recognizing handwritten text is a problem that can be traced back to the first automatic machines that needed to recognize individual characters in handwritten documents. Classifying handwritten text or numbers is important for many real-world scenarios. For example, a postal service can scan postal codes on envelopes to automate the grouping of envelopes which has to be sent to the same place. This article presents recognizing the handwritten digits (0 to 9) using the famous_ digits _data set from Scikit-Learn, using a classifier called Logistic Regression.

Scikit-Learn is a library for Python that contains numerous useful algorithms that can easily be implemented and altered for the purpose of classification and other machine learning tasks.

Prerequisites

If you already have Jupyter notebook and all the necessary python libraries and packages installed you are ready to get started.

If not you can use Google colab too!

Let us start by importing our libraries

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logistic-regression artificial-intelligence machine-learning digit-recognition scikit-learn

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