Building Handwritten Digits Recognizer using Support Vector Machine

Building Handwritten Digits Recognizer using Support Vector Machine

Building Handwritten Digits Recognizer using Support Vector Machine. We are going to use Support Vector Machines for predicting handwritten digits from MNIST data set.

Handwriting Recognition:

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. Think about, for example, the ZIP codes on letters at the post office and the automation needed to recognize these five digits. Perfect recognition of these codes is necessary in order to sort mail automatically and efficiently.

To address this issue in Python, the _scikit-learn _library provides a good example to better understand this technique, the issues involved, and the possibility of making predictions.

The problem we are solving in this blog involves predicting a numeric value, and then reading and interpreting an image that uses a handwritten font. So even in this case, we will have an estimator with the task of learning through a fit() function, and once it has reached a degree of predictive capability (a model sufficiently valid), it will produce a prediction with the predict() function. Then we will discuss the training set and validation set, created this time from a series of images.

The Digits Dataset

The _scikit-learn _library provides numerous datasets that are useful for testing many problems of data analysis and prediction of the results. Also in this case there is a dataset of images called Digits.

This dataset consists of 1,797 images that are 8x8 pixels in size

Let's start with importing the dataset from scikit-learn:

from sklearn import datasets   ### importing datasets from sklearn
digits = datasets.load_digits()  #### loading data from scikit_learn library
print(digits.DESCR)  ### getting information about the data

The images of the handwritten digits are contained in a digits.images array. Each element of this array is an image that is represented by an 8x8 matrix of numerical values that correspond to grayscale from white, with a value of 0, to black, with the value 15.

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