This document describes a student project to design a handwritten digit recognition system. The main objective is to build a model that can recognize handwritten digits from 0 to 9. The students used the MNIST dataset and explored models like LeNet, ResNet, VGGNet, GoogleNet and CNN. They achieved 98.2% accuracy using LeNet, which had the shortest training time of 7 minutes on average. In conclusion, the students successfully created a digit recognition program using neural networks and datasets.

1. HANDWRITTEN
DIGIT RECOGNITION
SYSTEM
By
Atyasha Das (B419019)
Barsha Priyadarsini (B419021)

2. AIM OF THE PROJECT
The main objective of this project is to
design a system which will recognise the
correct handwritten digits written by
humans.

3. PRE-REQUISITE TOOLS AND
LIBRARY
APPLICATIONS
1 2 3
i. Python Programming
ii. Basics of Machine
learning and deep
learning concepts
iii. Tkinter for GUI
i. PyCharm
ii. Jupyter Notebook
iii. Keras Library
iv. MNIST Dataset
v. Tkinter
vi. NumPy
i. Postal Mail Sorting
ii. Bank Cheque
Processing
iii. Form Data Entry

4. STEPS OF PROJECT
DEVELOPMENT
Training the model Evaluating the
model
Parameter tuning
Choosing a model
Preparation of data
Collection of data
1 2 3
4 5 6 7
Making predictions

5. LITERATURE
SURVEY

6. MNIST Dataset
 Among thousands of datasets available in the market, MNIST is the most popular
dataset for enthusiasts of machine learning and deep learning.
 Above 60,000 plus training images of handwritten digits from 0 to 9 and more
than 10,000 images for testing are present in the MNIST dataset. MNIST dataset
can be directly in- built in keras, we don’t have to download the dataset.

7. Network
Architectures

8. VGGNet
VGG stands for Visual Geometry Group; it is a standard deep
Convolutional Neural Network (CNN) architecture with multiple
layers. The “deep” refers to the number of layers.
Average Training Time: 15-30 mins
Accuracy: 99%

9. LeNet
It is a simple convolutional neural network that held the basis for
subsequent advancements in deep learning and image recognition.
Average Training Time: 5-10 mins
Accuracy: 98-99%

10. ResNet
ResNet, short for Residual Network, is a deep convolutional neural network
(CNN) architecture. ResNet architectures typically have hundreds or even
thousands of layers, enabling them to learn complex patterns and achieve state-
of-the-art results on various computer vision tasks, including image classification,
object detection, and image segmentation.
Average Training Time: 10-20 mins
Accuracy: 99.3-99.5%

11. GoogleNet
GoogLeNet, also known as Inception v1, is a deep convolutional
neural network (CNN) architecture developed by researchers at
Google. GoogLeNet, or Inception v1, has 22 layers in total. It includes
both convolutional layers and fully connected layers.
Average Training Time: Few hours to one day
Accuracy: 98-99%

12. CNN
Convolutional Neural Network is a type of artificial neural network
specifically designed for processing and analyzing structured grid-like
data, such as images or sequences.
Average Training Time: Few hours to one day
Accuracy: 98-99%

13. COMPARING ALL 5 NEURAL
NETWORKS

14. VGGNet
01.
04.
02.
05.
03.
LeNet
ResNet
CNN
GoogLeNet
AVG TIME TAKEN AVG ACCURACY
23 mins
7.1 mins
16.8 mins
14 hours
29 mins
98.75%
98.20%
99.38%
99.02%
98.90%
*these outcomes are based on our observation

15. RESULT
After learning and working with all the models we decided to work with the LeNet-5
model for our project and successfully build a digit recognition system with 98.2%
accuracy and an average training time of 7.1 minutes.
ResNet CNN
GoogLeNet
VGGNet
LeNet

16. CONCLUSION
We have successfully completed our project and built a program to determine
handwritten digits successfully using various datasets and neural networks.

17. THANK YOU!

18. 04.
section name

19. VGGNet
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20. ST QUARTER
January
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March
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2ND QUARTER
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May
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3RD QUARTER
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4TH QUARTER
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Comparing all 5 neural networks

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