CS 2206 Pattern Recognition – 2014/2015
Handout: Lab 2
Section contents:
 Some definitions
 Supervised vs. unsupervised learning
 PR OCR example
 Neural networks
 Neural network model
Some definitions
Pattern:
 It is an entity that could be given a name,
 It is an object that can be image, signal, waveforms or any type of measurements that need to be
classified.
Features:
Are measurable quantities obtained from the patterns and the classification task is based on their
respective values.
Feature vectors:
 A number of features (x1, x2, x3, … , xn), constitute the feature vector
𝑥1
𝑥2
𝑥 = [ ] ∈ 𝑅𝑛
…
𝑥𝑛
𝑥 = [𝑥1, 𝑥2, 𝑥3, … , 𝑥𝑛]𝑇
Where T is the transposition.
 Each of the feature vectors identifies uniquely a single pattern.
Classification:
Assigning unknown objects or patterns into the correct class.
Comparison between supervised & unsupervised pattern recognition:
Supervised PR
Unsupervised PR
A set of training data were available
training data of known class labels are not
available
The classifier was designed by exploiting this
Given a set of feature vectors (x) and the goal
a-priori known information
is to unravel the underlying similarities and
cluster “similar” vectors together.
Patterns whose class is known a-priori are used The number of classes in unknown and no
for training.
training patterns are available.
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Pattern Recognition for OCR
 Character (letter or number) recognition is another important area of pattern recognition, with
major implications in automation (computerization) and information handling.
 Optical character recognition (OCR) systems are already commercially available and more or less
familiar to all of us.
 An OCR system has a “front-end” device consisting of :
 light source,
 scan lens,
 document transport,
 detector.
 At the output of the light-sensitive detector, light-intensity variation is translated into “numbers”
and an image array is formed.
A Simple Pattern Recognition Problem (Statistical)
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A Simple Pattern Recognition Problem (Statistical)

Neural networks:
o
o
o Is a network that is represented by a set of nodes and arrows.
o A node correspond to a neuron.
o An error corresponds to a connection along with the direction of signal flow between
neurons.

Components of neural network model:
o A set of weights:
 Which characterize the strength of the synapse linking a neuron to another.
 Synaptic weight linking a neuron “j” to neuron “i” could be identified by either
“wij” or “wji”.
 A weight can take either a (+ve) or (-ve) value, which indicate an excitatory or an
inhibitory synapse.
o
An adder:
 For summing the input neuron’s modulated by the respective connection weights,
the operation constitute a linear combiner.
1
o

An activation function:
 Which is applied on the input summation for limiting the amplitude of the
neuron’s output.
Neural network model:
Neural networks:
 Is a network that is represented by a set of nodes and arrows.
 A node correspond to a neuron.
 An error corresponds to a connection along with the direction of signal flow between
neurons.
Components of neural network model:
 A set of weights:
o Which characterize the strength of the synapse linking a neuron to another.
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o Synaptic weight linking a neuron “j” to neuron “i” could be identified by either
“wij” or “wji”.
o A weight can take either a (+ve) or (-ve) value, which indicate an excitatory or
an inhibitory synapse.
 An adder:
For summing the input neuron’s modulated by the respective connection weights, the operation
constitute a linear combiner.
 An activation function:
Which is applied on the input summation for limiting the amplitude of the neuron’s output.
Neural network model:
Input patterns
x1
x2
x3
…
xm
Bias bk
Wk1
Output
Activation function
Wk2
Wk3
∑
Uk
𝑓 (. )
Yk
Wkm
Synaptic weights
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