This was our final year project in b.s Computer systems Engineering

2. Outlines:
 Introduction
 Methodology
 Results

3. Introduction :
 Our project is about Sign Language Recognition.
 Sign language is a way of communication
between the deaf community and normal
person.
 Gesture is a way of communication between
human and a computer.
 Our project is to recognize a set of hand’s
gestures.
 The gestures are classified using different
classifiers.
 The classifiers are IBK, Random Forest, Random
tree.

4. Edges detection:
 Canny edge detection:
• Canny edge detection is a popular
edge detection algorithm.
• It was developed by John F. canny
in 1986.
• It is a multi stage algorithm and
we will go through each stage.

5. Steps
1. Smoothing
2. Finding gradients
3. non-maximum suppression
4. Double thresholding
5. Edge tracking by hysteresis

6. Results: Edge detection using
Canny

7. Features extraction:
 Hu moments
 Principle component analysis

8. PCA:
 PCA stands for principal component analysis.
 It is a way of identifying patterns in data, and
expressing the data in such a way as to
highlight their similarities and differences.
 The advantage of PCA is that once you have
found these patterns in the data, and you
compress the data, without much loss of
information and it is used in image
compression.

9. Steps:
1. Get some data
2. Subtract the mean
3. Calculate the covariance matrix

10. Cont.
4. Calculate the eigenvectors and eigenvalues of the
covariance matrix
5. Choosing components and forming a feature vector
4. Deriving the new data set

11. Hu moments.
Moments are scalar quantities used to
characterize a function and capture its significant
features.
Hu moments used to describe, characterize and
quantify the shape of image.
Hu moments are normally extracted from the
outline of an object in an image.
Hu moments are invariant to rotation.

12. Methodology:
Edge detection
Features
extraction
Generating Arff
File
Passing Arff file to
classifiers
Collecting Data
Result

13. Results.
PCA Hu moments
1) Ibk 87.91% 75.41%
2) Random tree 80.83% 74.58%
3) Random Forest 86.66% 81.66%
4) Kstar 58% 57%

16. Prepared By:
Jamal Khan (11PWCSE0918)
Department of Computer
Systems Engineering
UET Peshawar
E-mail:engrjamalkhan0918@gmail.com
Contact No:
03219035609/03456253302