ECE 203: Introduction to MATLAB
Spring 2022: Final Project: Digit Recognition
Introduction
In this project, you will accomplish the task of handwritten digit recognition using a simple
distance based classification. The dataset is posted on Isidore as a part of this project
(“digit_data.mat”). This dataset comprises 5000 distinct handwritten images of numbers 0
– 9. The dataset in the MAT file is organized in such a way that first 500 images belong
to ‘0’, second 500 images belong to ‘1’ and so on. Please have a look at the starter code
provided in your resources.
Algorithmic Steps
1. Create a live script file called “run_digit_recognition.mlx”. Clear workspace and
then load the data. When you load the data, you will observe 2 variables named
images and labels being created in workspace.
2. Visualize every 500th image (1,501, 1001…etc.) to visualize each digit using
imagesc() and gray color map. Try to check the labels for those cases.
3. Now, split the dataset into a set of training and testing data. Use 60th image of each
digit for testing (1, 61, 121 and so on) and rest of the dataset is used for training
Make sure the size of testing images and training images are 20 × 20 × 84 and
20 × 20 × 4916 respectively.
4. Make sure to note the labels of training and testing data as well. Note that train labels
and test labels are of size 4916 × 1 and 84 × 1 respectively.
5. Now, write a nested loop in which you compute the Euclidean distance of each test
image from all training images. So distance would be a vector of size 4916 × 1 for
each test image.
6. Later, determine the case with minimum Euclidean distance for each test image and
note the corresponding index.
7. Determine the label with minimum Euclidean distance and that represents your
predicted label. Repeat this process for all test images.
8. Create another ‘for’ loop to display each test image and its closest match image
using subplot() and imagesc() with gray colormap. Do a help on commands if
necessary. Make sure to assign title to each subplot. Use a pause of 0.3 seconds.
You can check the video uploaded as part of this project for reference “Desired
results.avi”. You may also notice that the algorithm fails for a total of ‘6’ cases in
that video.
9. Now, determine the accuracy of your distance based classification algorithm using
the below mentioned formula. (Expected performance is around 92.86%).
???????? =
?????? ?? ????????? ?????????? ???? ???????
????? ?????? ?? ???? ???????
∗ 100
10.Write a detailed formal report. Make sure to include equations as necessary
(especially Euclidean distance). Make sure to include some example cases where
the prediction is correct and incorrect. Suggest methods that can help in improving
the accuracy.
Things to be submitted:
run_digit_recognition.mlx
run_digit_recognition.PDF (Live script exported to PDF)
Report (in pdf format)
