In this machine learning or AI project often we tend to begin to trying the written digit recognition problem. The digit recognition project deals with classifying data from the MNIST dataset. the info contains 60,000 pictures of 28x28 pixel written digits. By using image recognition techniques with a specific machine learning algorithmic rule, a program are often developed to accurately read the written digits within around ninety fifth accuracy. the speed are often even higher, counting on the chosen machine learning algorithmic rule.
Digit recognition system is that the working of a machine to train itself or recognizing the digits from totally different sources like emails, bank cheque, papers, images, etc. and in different real-world scenarios for on-line handwriting recognition on computer tablets or system, recognize variety plates of vehicles, process bank cheque amounts, numeric entries in forms filled up by hand (say — tax forms) then on
Machine learning and deep learning plays an important role in technology and AI. With the use of deep learning and machine learning, human effort are often reduced in recognizing, learning, predictions and lots of additional areas. this text presents recognizing the written digits (0 to 9) from the famous MNIST dataset, comparison classifiers like KNN, PSVM, NN and convolution neural network on basis of performance, accuracy, time, sensitivity, positive productivity, and specificity with using totally different parameters with the c
Keywords:
pattern recognition, written recognition, digit recognition, machine learning, WEKA, off-line written recognition, machine learning algorithmic rule, neural network, classification algorithmic rule.
Import the libraries
from keras.models import sequential
from keras.layers import Dense, Conv2D, Flatten
from keras.datasets import mnist
from keras.utils import to_categorical
import matplotlib.pyplot as plt
import numpy as np