Pattern recognition is a branch of machine learning focused on identifying patterns in data for classification and problem-solving, utilizing a system that includes components like sensing, segmentation, and classification. Machine learning allows computers to learn and adapt based on experience, categorized into supervised, unsupervised, and reinforcement learning. The future of pattern recognition remains a vibrant research area with applications spanning character recognition, medical diagnosis, and robotics.

2. Table of Content
1. Introduction to Pattern Recognition.
2. Pattern Recognition System.
3. Machine Learning.
4. Application of Pattern Recognition.
5. Future of Pattern Recognition.

3. Introduction to Pattern
Recognition
• Pattern Recognition is the branch of machine learning a computer science
which deals with the regularities and patterns in the data that can further
be used to classify and categorize the data with the help of Pattern
Recognition System.
• “The assignment of a physical object or event to one of several pre-
specified categories”-- Duda & Hart
• Pattern Recognition System is responsible for generating patterns and
similarities among given problem/data space, that can further be used to
generate solutions to complex problems effectively and efficiently.
• Certain problems that can be solved by humans, can also be made to be
solved by machine by using this process.

4. Pattern Recognition System
• This system comprises of mainly five components namely sensing,
segmentation, feature extraction, classification and post processing. All of
these together generates a System and works as follows.

5. 1. Sensing and Data Acquisition : It includes, various properties that describes
the object, such as its entities and attributes which are captured using
sensing device.
2. Segmentation : Data objects are segmented into smaller segments in this
step.
3. Feature Extraction : In this step, certain features of data objects such as
weight, colors, dimension etc. are extracted.
4. Classification : Based on the extracted features, data objects are classified.
5. Post Processing & Decision : Certain refinements and adjustments are done
as per the changes in features of the data objects which are in the process
of recognition. Thus, decision making can be done once, post processing is
completed.

6. Machine Learning
• According to Arthur Samuel, Machine Learning can be defined as the
“ computers that has ability to learn without being explicitly programmed”.
• It means that, machine learning is a branch of computer science which
enables computer systems to learn and respond to queries on the basis of
experience and knowledge rather than from predefined programs.
• It can be used to study data, construct algorithms for it and can further
make predictions on the data. Machine learning can be classified into three
categories, depending upon the nature of learning. These are:

7. SupervisedLearning
• When learning of a function can be done from its inputs and outputs, it is
called as supervised learning.
• One of the example of supervised learning is “Classification”. It classifies the
data on the basis of training set available and uses that data for classifying
new data.
• The class labels on the training data is known in advance which further helps
in data classification.
Un-SupervisedLearning
• When learning can be used to draw inference from some data set containing
input data, it is called as unsupervised learning.
• It clusters the data on the basis of similarities according to the
characteristics found in the data and grouping similar objects into clusters.
• The class labels on the training data is not known in advance i.e. no
predefined class.

8. ReinforcementLearning
• Reinforcement in general is, the action or process of establishing a pattern
of behaviour.
• Hence, Reinforcement learning is the ability of software agents to learn and
get reinforced by acting in environment i.e. learning from rewards.
• In reinforcement learning, the software agents acts upon the environment and
gets rewarded for its action after evaluation but is not told, of which action
was correct and helped it to achieve the goal.
For Example: Game Playing, Statistics.
• The problem of unsupervised learning involves learning patterns from the
inputs when specific output values are supplied.
• Clustering is an example of unsupervised learning which can further be used
on the basis of different methods as per requirements.

9. Application of Pattern
Recognition
• Pattern Recognition possesses a number a application in real world scenarios.
Some of them are listed below.
1. Character Recognition.
2. Weather Prediction.
3. Sonar Detection.
4. Image Processing.
5. Medical Diagnosis.
6. Speech Recognition.
7. Information Management Systems.

10. Future of Pattern Recognition
• Future of Pattern Recognition : It is a vast area of research and there are
endless number of possibilities as pattern recognition is still under research
as it is getting more futuristic and intelligent with a great affect in human
life in a positive manner.
• Since there are endless number of research options and some of them are
listed below.
1. Computer vision.
2. Natural Language Processing.
3. Game Playing.
4. Speech and Handwriting recognition.
5. Search engines.
6. Opinion mining.
7. Sequence mining.
8. Robotics.
9. Software engineering.
10. Medical Diagnosis.

11. A Brief Presentation on Pattern Recognition
By
The Minigranth Team.
To know more about Pattern Recognition
Visit us at : minigranth.com
E-mail : minigranth@gmail.com
Thank You !