Handwritten Digit Recognition Using CNN

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 04 | Apr 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 2572
Handwritten Digit Recognition Using CNN
Shubham Gorule1, Udit Chaturvedi2, Vignesh Naidu3, Aditya Burde4, Prof. Shyamala Mathi5
1,2,3,4 Students, Dept. of Electronics and Telecommunication Engineering, SIES GST, Nerul, Maharashtra, India
5Assistant Professor, Dept. of Electronics and Telecommunication Engineering, SIES GST, Nerul, Maharashtra,
India
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Abstract - Deep learning has recently taken a radical turn
in the field of machine learning by making it more artificially
intelligent, thanks to the advent of Convolutional Neural
Networks (CNN). Because of its wide range of applications,
deep learning is used in a wide range of industries, including
surveillance, health, medicine, sports, robots, and drones.
Handwritten Digit Recognition is an example of a computer's
capacity to recognise human handwritten digits. Because
handwritten numerals aren't flawless andmightbe generated
with a variety of tastes, it's difficult work for the machine. The
purpose of this project is to provide a response to a current
problem that uses a digit image and recognises the digit
contained in the image using the Convolutional Neural
Networks idea. The Modified National Institute of Standards
and Technology (MNIST) dataset is used to train our model in
this research. This datasetwascreatedusingtheconvolutional
neural network technique and Keras, a Python library for
intensive computation of neural nodes thatissupported bythe
Tensor Flow framework on the backend. We will be able to
estimate the handwritten digits in an image using this model.
This approach allows us to detect numerous digits.
Key Words: Digit Recognition, MNIST, CNN
1. INTRODUCTION
Handwritten digit recognition is currently used in a variety
of user authenticationapplications.Becausethehandwritten
numerals differ in size, thickness, style, and orientation.Asa
result, these obstacles must be overcome in order to solve
the problem in my project. We will be utilizinga uniqueform
of deep neural network called a Convolutional Neural
Network, which is used to analyze visual imagery by
converting massive amounts of pixel data into meaningful
data that can be sent as input layer data to an convolutional
Neural Network for training. After that, the system will
create a model for handwritten digit recognition using
hidden layers of CNN. On the Modified National Institute of
Standards and Technology (MNIST) dataset, which contains
70,000 photographs of handwritten digits, we will applya 7-
layer LeNet-5 ConvolutionNeural Network technique.Keras,
a Python-based neural network library,isused.The network
is trained using the stochastic gradientandback propagation
algorithms, and then tested using the forward method. Once
the model is ready, the user can upload a picture containing
digits and receive a proper forecast of their input.
1.1 Need of the project
Everything will be online in the future as we go into the
digital era, and handwritten digit recognition will be the
future. To create a handwritten digit recognitionsystemthat
uses a deep learning model to allow users to automate the
process of digit recognition. It is faster than traditional
typing and hence saves time.
1.2 Existing Systems
Handwritten digit recognition is used in a variety of sectors,
including the post mail sorting system, which queues
scanned images of mail envelopes and extracts the part
defining the postcode to be delivered.Sortingmails basedon
these postcodes according to their region can be done with
the help of a digit recognizer. Form processing is another
application that uses this technology. Digits are extracted
from certain columns of a form, and users apply filters to
acquire the desired results. However, there is no user
interface for having their photographs scanned and
recognised, making the operation difficult to use for the
average user.
1.3 Scope
Handwritten digit recognition with a classifier offers a wide
range of applications and uses, includingonlinehandwriting
recognition on computer tablets, recognising zip codes on
mail for postal mail sorting, processingbank check amounts,
numeric entries in handwritten forms, and so on. When
attempting to address this problem, there are a variety of
obstacles to overcome. The size, thickness, orientation, and
position of the handwritten numbers in relation to the
margins are not always consistent. Our goal is to create a
pattern classification algorithmthatcanrecognisetheuser's
handwritten numbers.Theresemblance betweendigitssuch
as 1 and 7, 5 and 6, 3 and 8, 8 and 8, and so on was the
general challenge we thought we would meet in this digit
categorizationproblem.Furthermore,peoplewritethesame
digits in a variety of ways. Finally, the individuality and
variation of each person's handwriting has an impact on the
construction and look of the digits.
2. Literature Survey
Rohan Sethi & Ila Kaushik, et al., June 2020 [1] - describe
optical digit identification in general and the stages
required, such as picture acquisition, pre-processing,
segmentation, feature extraction, classification, and post-
processing. We referenced a number of other articles,
including [2], [3], and [4], which assisted us in obtaining
fresh information. The articlethengoesonto explainseveral
classification methods such as the Naive Bayes Classifier,
Nearest Neighbor, Logistic Regression, Decision Trees,

Random Forest, and Neural Network. They used the KNN
Classification Algorithm in thepaper,wheretheydiscussthe
KNN architecture, steps involved in the algorithm, using the
Euclidean distance formula tocomputethenearestandmost
accurate labelled data so as to correctly classify the digits
from the dataset, providing input from the MNIST database
with moderately high accuracyoutputandlesscomputation,
and providing input from the MNIST database with
moderately high accuracy output and less computation. 2)
Anchit Shrivastava, Isha Jaggi, et al., Oct 2019 [2] discusses
multilayer neural networks, which include preprocessing,
feature extraction, and classification. They use the MNIST
database to go over the many types of features used in
feature extraction, such as structural characteristics,
modified edge maps, image projections, and so on.
Multi-zoning, concavitymeasurement,andgradientfeatures
are all available. We also grasped the concept of
multilayering from [8] [6]. They also discuss the error rate
associated with various sorts of characteristics. They go on
to explain convolution neural networks and how SVM is
utilised as a classifier. To boost recognition accuracy, the
training photos are retrieved from Alex-Net and multiple
augmentation approaches are applied. 3) Aug 2020 [3] –
Jinze Li, Gongbo Sun, Leiye, Qian Cao, et al. This research
primarily offers a handwritten digit recognition system
based on convolutional neural networks and deep
learning[7]. The system uses the MNISTdatasetasa training
sample and uses the Opencv tools to pre-process the image.
The Open CV Toolkit is a free and open source software
library. OpenCV is utilised to perform image 13 pre-
processing and feature extraction in this work. Then it
utilises LeNet-5, a convolutional neural network with five
layers, to extract the handwritten digit image features,
convolution pooling repeatedly, and pulling the resultintoa
one-dimensional vector.Finally, using the Softmax
regression model, which is a generalised logistic regression
model, identify the highest probability point to determine
the result in order to perform handwrittendigitrecognition.
4) Yawei Hou & Huailin Zhao, Feb 2014[6] - shows that
employing a simple model results in higher recognition.
They investigate the back progression neural network and
the convolution neural network (CNN) individually before
presenting a strategy that combines the two andusesa deep
neural network to improve recognitionaccuracymarginally.
It begins with a brief explanation, followed by CNN.
3. Literature Survey
3.1 Design Software
Python was chosen because it is a readable language.
Because the Python code is straightforward to grasp, it was
simple to create machine learning models. Python also
contains a vast number of libraries and frameworks. The
libraries keras, Tensor Flow, and NumPy were utilised.
Python is a high-level general-purpose programming
language that is interpreted. The use of considerable
indentation in its design philosophy emphasises code
readability. Its language elements and object-oriented
approach are aimed at assisting programmers in writing
clear, logical code for both small and large-scale projects.
Keras is the most widely used machine learning library; it is
an open source software with a Python interface.Itservesas
a Tensor Flow interface.It's also simple to use. TensorFlow -
For handwritten digit categorization, TensorFlow can train
and execute deep neural networks. With the same models
used for training, TensorFlow can predict production at
scale. NumPy libraries — it's a fantastic tool for learning
about machine learning, as well as mathematics and
scientific calculations. NumPy is in charge of all
mathematical tasks.
3.2 Dataset - MNIST
Photo credit – Wikipedia
The MNIST database (Modified National Institute of
Standards and Technology database) may contain a
significant number of handwritten documents. There are
60,000 training photos and 10,000 testing images in the
database. It's a popular dataset that's also fully free. The
error rate is also lower when compared to other data sets
used in machine learning. The MNIST dataset's images have
all been transformed to 28*28 pixels. Because it has a far
higher accuracy rate than other datasets, the MNIST dataset
is widely used across multiple machine learning languages.
Using a committee of neural networks, some researchers
have obtained "near-human performance" on the MNIST
database; in the same study, the authors achieve
performance double that of humans on other recognition
tests. The database's maximum error rate, according to the
original website, is 12%, which was attained using a simple
linear classifier with no preprocessing. Researchers usinga
new classifier called the LIRA, which may be a neural
classifier with three neuron layers based on Rosenblatt's
perceptron principles, obtained a best-case error rate of
0.42 percent on the database in 2004.Some researchers put
artificial intelligence systems to the test by putting a
database through random distortions. Thesesystemscanbe
highly successful at times.

3.3 Methodology
Because of its great accuracy, Convolutional Neural
Networks (CNNs) are utilized in imageanddigit recognition.
The fundamental advantage of CNN over its predecessors is
that it automatically discovers significant features without
the need for human intervention. As a result, CNN would be
an excellent choice for digit and image classification tasks.
A neural network is a hardware and/or software system
modelled after the way neurons in the human brain work.
Traditional neural networks aren't designed for image
processing and must be fed images in smaller chunks.
CNN's "neurons" are structured more like those in the
frontal lobe, the area in humans and other animals
responsible for processing visual inputs. Traditional neural
networks' piecemeal image processing difficulty is avoided
by arranging the layers of neurons in such a way that they
span the whole visual field. The proposed system model's
data flow diagram. There are two ways to give the system
information. The user can either provide an image of the
digit he wishes to detect or MNIST dataset data.
Preprocessing is done on the input photos. The accuracy of
recognised digits is compared using differentclassifiers,and
a result is obtained. The correctness of the results is given
alongside them. The first step is to place the dataset, which
can be done quickly and easily using theKerasprogramming
interface. The pictures in the MNIST dataset are in the form
of a cluster, which consists of 28x28 values that make up an
image and their labels.If there is a chance that the testing
photos will appear, this is equal. The pixels are represented
by a set of 784-d pixels with a range of 0 to 255, with 0 being
black and 255 denoting white. Python is a high-level
programming languagethatiswidelyusedaroundtheworld.
Its linguistic style allowssoftware engineerstocommunicate
concepts in fewer lines of code, and it was introduced
particularly for prominence on code. Python is a
programming languagethat allowsyoutowork morequickly
and efficiently using frameworks.
3.4 Architecture
Photo credit - Nikita Sharma/ConfusedCoder.com/January
2019
The image is compressed to 28 by 28 pixels in the first step.
This compression is required in order to obtain quick
results. Convolution 1 is the second step, which is kernel
valid padding, which means there is no padding at all. The
supplied image is preserved in its original state.Theimage is
compressed to 24 by 24 pixels once again in this step. The
image is further compressed to 12 by 12 pixels using max
pooling. After that, the image is reduced down to 4 x 4 pixels
using a convolution filter with maximum pooling. Finally,
ReLu is activated before to CNN.Every pixel in the ReLu
activation process is given a 0 or 1 value based on the
preceding filters procedure. These values are then flattened
before being transmitted to the MNIST training dataset. We
acquire our result after comparing it to its data set.
3.5 Implementation
From (Fig 1) the digits are written on the paper and then
uploaded to the output.

In (Fig 2) we can see that the digits are successfully
recognized by the model. We can detect multiple digits. The
accuracy of our model is about 90% based on 50 results.
4. CONCLUSION
On handwritten digit recognition, we have successfully
constructed a Python deep learning project. We createdand
trained a Convolutional Neural Network (CNN) that is
exceptionally good at picture classification. Handwritten
Digit Recognition using Deep Learning Methods has been
developed. In order to provide a comparison of the
classifiers, the most frequently used Machine learning
algorithms CNN were trained and tested on thesamedata.A
great level of accuracy can be achieved with these deep
learning approaches. Unlike other research methodologies,
this one focuses on which classifier performs best by
increasing the accuracy. A CNN model with Keras as the
backend and TensorFlow as the software can achieve 90
percent accuracy.
REFERENCES
1) R. Sethi and I. Kaushik, "Hand Written Digit Recognition
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Conference on Communication Systems and Network
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"Handwritten Digit Recognition Using Machine Learning: A
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Energy, Environment and Intelligent Control (PEEIC), 2019,
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Handwritten Digit Recognition Using CNN

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