This document discusses several papers on handwritten digit recognition using machine learning algorithms. It provides the paper names, authors, publish years, methodologies used such as support vector machine, naive bayes, convolutional neural networks, and findings or limitations of each paper. The methodologies examined accuracy of algorithms in recognizing handwritten digits while limitations included varying individual writing styles and difficulty improving accuracy.

1. Hand Written Digit
Recognition
Course Title: Research & Innovation.
Course Code: CSE-326
Course Teacher: Meherunnesa Tania
Lecturer
Daffodil International University

2. Mir Shamim
Nahid
ID: 201-15-13919
Ishrat Suchita
ID: 201-15-13709
Sabrina Akter
Sorna
ID: 201-15-13714
Mubtasim Fuad
Opee
ID: 201-15-
13903
Sornali Khatun
Bonna
ID: 201-15-
14202
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3. Paper Name Author Publish
Year
Methodology Finding/Limitation
Handwritten Digit
Recognition using Machine
Learning Algorithms
S M Shamim,
Mohammad Badrul
Alam Miah, Masud
Rana & so on.
2018 Algorithms are used in this
paper Support
Vector Machine, Naïve Bayes,
Bayes Net, Random Forest, J48
and Random Tree has been
used
for the recognition of digits
Large variation of individual
writing styles. In addition the
the curves are not
necessarily
smooth like the printed
characters. So it is difficult to
improve accuracy
Handwritten Digit
Recognition Using Machine
Learning
Rabia KARAKAYA,
Serap KAZAN
2021 Algorithms are used in this paper
Support Vector
Machine (SVM), Decision Tree,
Random Forest, Artificial Neural
Networks (ANN), K-Nearest
Neighbor (KNN) and K- Means
Algorithm.
Handwritten texts in the
surrounding
area can be quickly scanned
and processed via the
phone camera.
Handwritten Digit
Recognition With Machine
Learning Algorithms
Kübra Demirkaya,
Ünal Çavuşoğlu
2021 Linear Regression, Machine
Learning aproach: CNN
Library: TensorFlow,
Scikit Learn, Keras and
Numpy
Difficult to improve accuracy.
accuracy. Accuracy is low.

4. Paper Name Author Publish
Year
Methodology Finding/Limitation
Handwritten Digit
Recognition Using Various
Machine Learning
Algorithms and Models
Pranit Patil,
Bhupinder Kaur
2020 Study the efficiency of
quantum computing using
Grover Algorithm and KNearest
Algorithm. Comparison of
three classification algorithm In
other terms Multilayer
Perceptron (MLP), Naïve
Bayes(NB), and K-Star.
it is a more accurate
classification tool and it
results in binary
classification or
regression challenges.
Offline Handwritten Digits
Recognition Using Machine
learning
Shengfeng Chen ,
Rabia Almamlook
Yuwen Gu, Dr. Lee
wells
2018 Five machine learning classifier
models namely Neural
Network, K-Nearest
Neighbor (KNN), Random
Forest, Decision Tree and
Bagging with gradient boost.
This paper have some
advantage image
processing techniques,
median filter, binary, and
sharpening improve
image quality.
Handwritten Digit
Recognition using Machine
and Deep Learning
Algorithms
Ritik Dixit, Rishika
Kushwah, Samay
Pashine
2021 We have performed using
Support Vector
Machines (SVM), Multi-
Layer Perceptron (MLP) and
Convolution Neural
Network (CNN) models.
The characteristic chart
of each algorithm on
common grounds like
dataset.

5. Paper Name Author Publish
Year
Methodology Finding/Limitation
Handwritten Digit
Recognition Using Machine
Learning
Algorithms
S. M. Shamim,
Mohammad Badrul
Alam Miah,
Sarker, Masud
Rana, Abdullah Al
Jobair
2018 Pattern recognition,
Handwritten recognition,
Digit recognition,
Machine learning, Off-line
handwritten recognition,
Machine learning algorithm.
Handwritten Digit
Recognition
Priyanshu Singh,
Pranali Pawar,
Nikhil Raj
2022 SVM,KNN,RFC,CNN
HANDWRITTEN DIGIT
RECOGNITION SYSTEM
USING
MACHINE LEARNING
Apaar Chadha,
Gaurav Yadav,
Keshav Ahlawat
2022 Proximal SVM, Multilayer
Perceptron ,SVM, Random
Forest, Bayes Net, Naive
Bayes

6. Paper Name Author Publish
Year
Methodology Finding/Limitation
Handwritten Digit
Recognition Using Deep
Learning
A. Y. Maghari,
Jürgen
Schmidhuber,
Ahlawat S
2022 Convolutional Neural
Network, Linear Regression
Machine Learning
Algorithms, Image analysis
and feature extraction
Difficult to improve
accuracy. Accuracy is low.
Handwritten Digit
Recognition using CNN
Al Maadeed,
Somaya, and
Abdelaali Hassaine,
R.G.Mihalyi
2019 K-Nearest Neighbor (KNN),
Random Forest Classifier
(RFC), Support Vector
Machine(SVM)
Utilising these deep learning
techniques, a high amount
of accuracy can be obtained.
HANDWRITTEN DIGIT
RECOGNITION USING
OPENCV AND CNN
Li Deng , Vineet
Singh, Retno
Larasati, L. Bottou
2021 Ensemble neural networks
that combined with
ensemble decision tree ,
PCA Principal component
analysis, Simple Neural
network and back
propagation
Aim of this paper is to
facilitate for recognition
of handwritten numeral
using specific standard
classification techniques

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