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1. Pattern Recognition
Abstract:
Informally, a pattern is defined by the common denominator among the multiple
instances of an entity. Such as, commonality in all fingerprint images defines the
fingerprint pattern, Thus, a pattern could be a fingerprint image, a handwritten cursive
word, a human face, a speech signal, a bar code, or a web page on the Internet. Often,
individual patterns may be grouped into a category based on their common properties,
the resultant group is also a pattern and is often called a pattern class. Pattern
recognition is the science for observing the environment, learning to distinguish patterns
of interest from their background, and making sound decisions about the patterns or
pattern classes.
Basic concept of pattern recognition
Introduction:
in this subject we will know somethings about pattern recognition , such as what is it
and how can we use it and recognize objects, pattern recognition is useful for us, we
can find it in mobiles, camera, and computers and it helps us to recognize about
everything, we can recognize object by features that extracted from the object, this
research is going to discuss What is pattern recognition, Object and how to recognize it,
What are types of objects, Features of objects, The general block diagrams, What is
processing steps.
1. The pattern recognition:
- Pattern recognition first we should know what pattern is, pattern is description of
an object, to discriminate the objects this pattern based on human view, so the
description is, represented by a set of measurements, such as barcode,
footprint.
There are kinds of pattern such as visual patterns, temporal patterns, and logical
patterns. Now we defined pattern, so what is recognition? Recognition is
identification of a pattern to be specified into category. Kinds of recognition such
as classification and clustering. Now we can define pattern recognition as the
assignment of a physical object, and the goal of pattern recognition is based on
the classification of objects.
2. how to recognize objects:
- We can recognize objects by using pattern recognition before it we should know
three elements for creating any application by the machine based on pattern
recognition, such as perceive, process and prediction. Objects maybe be images
or signals, or any measurement are needed to be classified, we can recognize
objects by applications of pattern recognition. There are steps to recognize
objects such as: preprocessing like filtering and segmentation, feature
extraction, hypothesize object, verify objects.
3. Types of objects:
There are many types of objects that can be applicable one of them is: face
recognition, understand spoken words, Fingerprint, Reading handwriting.

2. 4. Features:
- Attributes which characterize properties of the samples.
- Such as: height, weight, and age, … etc.
- Features can be face or hands or anything in the human.
- There are extensions to features, like feature vector and feature space.
A. Feature Vector: vector formed by a group of features.
B. Feature Space: space containing all the possible feature vectors.
- We can select features that based on simple to extract, invariant to irrelevant,
insensitive to noise.
- A good extracted feature lead to a high quality of a feature vector.
5. The general block diagram:
1) Sensing: physical inputs converts into digital signal data by sensor.
2) Segmentation: its isolate sensed objects from the background.
3) Feature extraction: Feature extractor steps object properties.
4) Classification: its use extracted features to set the sensed object into
selected category.
5) Post processing: post processor decides an appropriate action that based
on the classification.
6. We can use it in:
Inside medical science, pattern recognition is the basis for computer-aided
diagnosis systems. CAD describes a action that supports the doctor's
explanations and findings. Other applications of pattern recognition techniques
are automatic speech recognition, classification of text into several categories, the
automatic recognition of handwriting, automatic recognition of images of human
faces, or handwriting image extraction from medical forms. The last two examples
form the subtopic image analysis of pattern recognition that deals with digital
images as input to pattern recognition systems.
Optical character recognition is a classic example of the application of a pattern
classifier, see OCR-example. The method of signing one's name was captured
with stylus and overlay starting in 1990. The strokes, speed, relative min, relative
max, acceleration and pressure is used to uniquely identify and confirm identity.
Banks were first offered this technology but were content to collect from the FDIC
for any bank fraud and did not want to inconvenience customers.
Artificial neural networks and deep learning have many real-world applications in
image processing, a few examples:
identification and authentication: fingerprint analysis, face detection, verification
and voice-based authentication.
medical diagnosis: screening for cervical cancer, breast tumors or heart sounds;
defense: various navigation and guidance systems, target recognition systems,
shape recognition technology etc.
Conclusion:
We can recognize people or objects by features, and we can get feature by pattern
recognition, we can know people by recognize their features like faces, hand, and eyes,
when you extract the features, you go through some steps and processes to get the
results.

3. Pattern Recognition
Applications of pattern recognition
Introduction:
In the pattern recognition there are some applications that can recognize the objects,
these applications work in certain steps, These programs we use in our working lives
we use them in a lot of things like phones or computers in this subject we will explain in
a short, What are these programs and what they mean and how they work,. This page
includes a technical definition of OCR. It explains in what OCR means and is one of
many software programs.
1. The applications of pattern recognition:
- Applications of pattern recognition that application we use to recognize
everything, then it makes some steps to get information from the objects.
2. Types of the applications:
a) Character Recognition:
used to convert images with characters to the identified character strings.
b) Speech Recognition:
used to convert acoustic signal to contents of the speech.
c) Fingerprint Recognition:
Using fingerprints of some person to get the person’s identity.
d) Signature Verification:
In this app u should input your signature of person to get the signatory’s identity.
e) Face Detection:
Input images with several people to get location of them.
f) Text Categorization:
Input document to get category of the text.
3. OCR:
OCR is one of the application patterns, is so important to us, its abbreviation of
optical character recognition, it is the electronic conversion of images of a text to the
string of characters, it can scan text from images of handwritten. The process of
OCR is most commonly used to turn hard copy legal or historic documents into
PDFs. Once placed in this soft copy, users can edit, format and search the
document as if it was created with a word processor.
4. How the OCR work?
The first step of OCR is using a scanner to process the physical form of a document.
Once all pages are copied, OCR software converts the document into a two color,
the scanned-in image is analyzed for light and dark areas, where the dark areas are
identified as characters that need to be recognized and light areas are identified as

4. background. The dark areas are then processed further to find alphabetic letters or
numeric digits. OCR programs can vary in their techniques, but typically involve
targeting one character, word or block of text at a time. Characters are then
identified using one of two algorithms:
Pattern recognition- OCR programs are fed examples of text in various fonts and
formats which are then used to compare, and recognize, characters in the scanned
document.
Feature detection- OCR programs apply rules regarding the features of a specific
letter or number to recognize characters in the scanned document. Features could
include the number of corner lines, crossed lines or curves in a character for
comparison.
When a character is identified, it is converted into an ASCII code that can be used
by computer systems to cope with more manipulations.
5. The technology of OCR:
OCR is composed entirely of three main components such as: scanner, OCR
software and prespecified samples, The OCR program communicate with other
components for the document to be stored on your computer. The OCR technology
is used in office, education and anything, and users can be able to convert the
surveys and contracts.
Benefits of optical character recognition:
The main advantages of OCR technology are saved time, reduced errors, and
minimized effort.
History of OCR:
Early optical character recognition may be traced to technologies involving
telegraphy and creating reading devices for the blind. In 1914, Emanuel
Goldberg developed a machine that read characters and converted them into
standard telegraph code. Concurrently, Edmund Fournier d'Albe developed
the Otophone, a handheld scanner that when moved across a printed page,
produced tones that corresponded to specific letters or characters.
Conclusion:
There are a lot of programs of the category of patterns used in our working lives, we
use them in different types of fields and places these programs help us to identify a
lot of things like: people or animals or devices, and also benefit us in identifying
people's faces and people's voices and fingerprints and some important programs
such as OCR this program is used in converting written images into an electronic
component.

5. Pattern Recognition
Pattern recognition process (case study)
Introduction:
Pattern recognition systems consist of four functional units: A feature extractor to select
and measure the representative properties of raw input data in a reduced form, and a
pattern matcher to compare an input pattern to reference patterns using a distance
measure, and a reference templates memory, and a decision maker to make the final
decision, so I’m going to explain The Automation System process, then we will know
how it work and how can we classify between two toys or anything but robot , and how
can we design a robot , and what is the general block diagram that required to the robot,
and what is the important issues that maybe occur during implementation and how can
we solve it.
1. The Automation System of pattern recognition:
The automation system based on four important components such as: conveyor belt,
two conveyor belts, robotic arm, CCD camera, computer.
A) Conveyor belt: for incoming products
B) Two conveyor belts: for sorted products
C) Robotic arm: to pick‐and‐place
D) CCD camera: for a vision system
E) Computer: to analyze images and control the robot arm
2. The process’s steps:
Step 1: preprocessing
Step 2: Feature extraction
Step 3: Classification
i. Preprocessing:
The purpose of preprocessing for reducing the noise without losing relevant
information, it through on some steps such as: image processing and
segmentation, image processing used for removing noising and enhance the
level of contrast, segmentation used for isolate different objects from one another
like car and airplane.
ii. Feature extraction:
one of the most important steps in the pattern recognition system design, this
step is done by measuring and selecting some features or properties of the
object to be classified.it uses for extract features from the preprocessed image,
features like as length or lightness.
iii. Classification:
It used to evaluate the measurements of the feature and make a decision, the
purpose of this step to distinguish different types of objects.

6. 3. Important issues in pattern recognition: There are many important issues occur
during the implementation such as noise, segmentation, data collection, feature
extraction, missing features, model selection, over fitting, classifier ensemble and
costs and risks and computation complexity.
A) Noise:
There are noises in the pattern recognition process like shadows, conveyor belt
might shake, and noise can reduce the reliability of the features that measured,
we can solve it by the knowledge of the noise process.
B) Missing Features:
The values of features can be missed, we can solve it by train classifiers with
missing features, there are two solution to solve it.
First is Naïve method, that can be used but maybe not optimal, it used for
assuming the value of missing features is zero and assigning the average value
of patterns.
Second is Sophisticated method and it might be better, but it needs extra efforts
in terms of storage, it fills in the missing values with regression techniques.
C) Overfitting:
We can get best complexity by using normal complex because if I use more
complex than necessary it let me to lead to overfitting.
D) Context: can be used to improve the classifier.
E) Costs and risks: Cost is the loss after making incorrect decisions and there are
two types of cost such as equal cost and unequal cost, and risk is total expected
cost which we want to optimize error rate and we can solve it by using classifier
because classifier might be minimize some of total expected cost or risk.
F) Computational Complexity: Some approaches can perform to perfect
classification according to practical time and the storage requirements available.
G) Feature extraction:
Good extracted features make the job of the classifier fiddling, to get a small set
of candidate features available we take in consideration the following points, first
choose those are simple to extract and choose those are robust to noise and
choose those can lead to simpler decision boundaries.
Conclusion:
In the summary of this topic is how to recognize the airplane and the car through
the automation system of pattern recognition, which was based on 4 important
rules including camera, computer, etc. And there are some important steps that
the device recognizes on object, such as preprocessing and feature extraction
and classification and we know that preprocessing can reduce the noise of the
image and analyze and reformulate it and the most important problems in the
system such as noise and be solved by noise and reduce and lost features are
solved by training classifiers with the missing features and overfitting is solved by
getting a normal complex and also context used to improve the classifier.

7. Pattern Recognition
Linear classifier
Introduction:
In the field of machine learning, the purpose of statistical classification is to use an
object's characteristics to identify which class/category it belongs to, and linear classifier
makes this by making a classification decision based on the value of a linear
combination of the properties. feature values usually presented to the machine in
feature vector, so we will know what linear classifier is and what linear classifier’s
structure and processing steps of it.
1. Linear classification:
A classification algorithm (Classifier) that makes its classification based on a linear
predictor function combining a set of weights with the feature vector, where is a real
vector of weights and f is a function that converts the dot product of the two vectors
into the desired output, The weight vector is learned from a set of labeled training
samples.
A) Feature extraction:
The feature probably extracted by using data about the observed pattern.
2. classifier’s structure:
based on a linear predictor function combining a set of weights with the feature
vector.
3. Processing’s steps with algorithm:
gk(x) = p (wk|x)
gk(x) = wkT x +w0
wk weight vector,
w0 threshold weight
The feature vector X is a point in feature space
The classifier partitions the feature vectors into decision region

8. 4. How the learning satisfied:
By using Supervised learning and Training, in supervised System is made with a set
of training pairs consisting of an input vector x and desired output vector y(t). weight
(w) are adjusted to minimize the difference error between the actual output and
desired output y(t).
In training we can deterministic function classifying vectors by w(t+1) = w(t) + p(y(t) –
y^(t)) x(t).
Conclusion:
Finally, In the machine learning , we can identify which class belongs to by using
object’s characteristics in statistical classification, then the linear classifier makes a
classification decision that based on the value of a linear combination of the properties
and feature values presented to the machine, so linear classification that makes its
classification based on a linear predictor, then the feature extraction can be extracted by
using data about the observed pattern, we can say that classifier’s structure based on a
linear predictor function, we can know the learning satisfied by two steps supervised
and training.
Conclusion:
Pattern recognition is not a new field of research, in fact, theories and techniques that
has been developed for a long time. With the rapid advancement of computers,
architecture, machine learning, and computer vision, it is possible to deal with
computational complexity and to bring more and more new ways of thinking into the
research of pattern recognition. In this article, I would like to Introduce the basic method
Concept, compact explanation, widely used methods of pattern recognition, and some
Outstanding applications shall be included.

9. List of references:
https://en.wikipedia.org/wiki/Pattern_recognition R.O. Duda, P.E. Hart: Pattern
Classification and Scene Analysis. John Wiley & Sons, Inc., 1973.
https://en.wikipedia.org/wiki/Optical_character_recognition
https://searchcontentmanagement.techtarget.com/definition/OCR-optical-character-recognition.
http://compneurosci.com/wiki/images/c/c0/Linear_Classification.pdf