Pattern recognition in computer science

Pattern recognition in computer science
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Pattern recognition is a vital area of computer science that enables the recognition of patterns in datasets. It employs algorithms in various applications, such as recognizing text, images, and human behavior and predicting stock market trends. These algorithms classify data, form clusters, and identify outliers and trends in machine learning, AI, and NLP. Pattern recognition is paramount in helping machines make decisions and solve complex issues. This article will explain pattern recognition’s fundamentals, applications, and significance in computer science.

What does computational thinking mean by pattern recognition?

Pattern recognition is integral to computational thinking to comprehend complex matters more efficiently. By recognizing patterns within a sequence or dataset, computational thinking allows for more straightforward comprehension and problem-solving. Additionally, reusing solutions to address similar issues can increase the efficiency of the problem-solving process. Pattern recognition aids in finding components of other problems that are solved with existing solutions, even if the complete computing solution cannot be replicated for a new purpose.

Why do patterns exist?

Let’s say we want to create a collection of cat drawings.

Cats all have similar qualities. They all share features like eyes, tails, and hair. They enjoy meowing and eating fish, too.

Because we know that all cats have eyes, tails, and hair, we may successfully depict a cat by merely presenting these universal features.

These qualities are patterns in computational thinking. Following this pattern, we can describe additional cats once we recognize one. The particulars are the only thing that differs:

  • One cat could have black fur, a long tail, and emerald eyes.
  • Another cat could have striped hair, golden eyes, and a short tail.

Types of pattern recognition

Let’s see the types of pattern recognition below:

  • Statistical Procedures
  • Syntactic Strategies
  • Template-Matching 
  • Neural Network Approach 
  • Fuzzy Model 
  • Hybrid Model

Here are three of the most well-known, and this article thoroughly explains the remaining options.

Statistical pattern recognition

Statistical pattern recognition includes gathering, analyzing, and processing data to establish general principles or notions applied to new, unseen data.

Syntactic pattern recognition 

This technique aids in recognizing complex patterns that necessitate a hierarchical methodology. For example, this approach can distinguish alphabet letters and identify their correlations.

Neural networks for pattern recognition

The most common method used now to find patterns is neural networks for pattern recognition. A computing system called an Artificial Neural Network (ANN) is based on the neural network in the human brain. An ANN effectively processes incoming data and learns to detect patterns in various kinds of data, including textual, visual, and audio. Regrettably, this approach is the most expensive of the bunch.

Pattern recognition technology 

One of the following two tasks is to recognize and classify a pattern: 

Supervised Categorization: The input pattern is recognized via supervised classification as a member of a preset class. 

Unsupervised Categorization: Designates a previously unspecified class to the input pattern. 

Pattern recognition is often represented as a classification or categorization task. This is done by having the system create the classifications (supervised categorization) or finding patterns through similarity (unsupervised classification). To learn more about the two, look at our post on supervised and unsupervised learning. Research and development into more complex, computationally intense tasks drives pattern recognition forward.

Pattern recognition examples in daily life

We instinctively employ pattern recognition in our daily lives, even though it is most frequently considered a stage in computational thinking.


Driving along, our ability to recognize patterns helps us to anticipate and respond to various traffic patterns. For example, when a traffic signal turns yellow, we can identify the standard sequence of crossing traffic turning first, then cross traffic going straight, followed by the lanes turning in our direction before our light turns green. Understanding this pattern lets us guess how long before the signal turns green.

Social Media Intelligence (SMI)

Social media intelligence (SMI) security applications for pattern identification are widely used. For example, users suspected of criminal activity, civil unrest, or religious extremism are identified through social networks. This vast amount of data created every minute can be effectively analyzed through AI to provide valuable information. Facebook uses pattern recognition to identify false accounts and stop the misuse of profile pictures. Furthermore, pattern recognition also works to understand the context of conversations, detect inappropriate language, and prevent disrespectful behavior. But, it can see posts that may indicate suicidal thoughts.


A standard, machine-readable bar code known as UPC, or universal product code, is used to identify items bought in grocery shops and other retail establishments.

The stock-keeping unit (SKU) level of the individual items is encoded by UPCs, enabling a producer or retailer to keep track of the number of units sold over a predetermined time frame. Just-in-time inventory management may benefit from this kind of tracking. The Universal Code Council (UCC), a nonprofit organization headquartered in Lawrenceville, New Jersey, USA, maintains the Universal Product Code (UPC). The UCC, established in 1972, manages the UPC for more than 200,000 businesses worldwide.

Processing text

Text processing is a vital tool in business to optimize customer service. We use various technologies to gain insight from customer feedback and product reviews. Using this, we can categorize text by its topic, keywords, sentiment, and purpose. Furthermore, text processing is a critical factor for competition and market research. It automatically extracts entities such as businesses, individuals, brands, locations, and other proper marketing and business development information.


Pattern recognition is an essential tool in medicine. By recognizing patterns, doctors can better comprehend illnesses and their potential sources and diagnose and treat diseases. Tests and doctor’s notes are compared to existing patterns to identify conditions from a patient’s symptoms. Furthermore, new infections are classified based on past medical issues, and treatments, preventions, or cures are found.


Robotics involves designing, constructing, and applying robots for tasks that humans have traditionally performed. Automotive manufacturing and hazardous environments are common areas where robots work to complete repetitive activities. Robots can possess senses similar to humans, such as sight, touch, and temperature sensing. Artificial intelligence works in various aspects of robotics, allowing them to make fundamental decisions. Robotics research aims to create self-sufficient robots that can move and make decisions in unpredictable settings. Today’s industrial robots are not humanoid, but an android is a robot resembling a human.

Mathematical formulas

They are using formulae in mathematics in everyday life. Tipping, converting measures, and calculating a car’s MPG are all examples of using pattern recognition. All mathematical formulae are the result of algorithmic thinking and pattern recognition. To begin with, identify patterns used to generate a standard response and proceed with them. They similarly used formulae in mathematics to recognize ways to decide if a particular method will benefit a given context.


Machine learning-based security solutions can detect patterns and adjust to changing user behavior to thwart hacker activity and future attacks. Cybersecurity teams can stay abreast of the newest threats and react to ongoing attacks with immediate insight into malware trends.

QR code

A QR Code, or Quick Response Code, is a printed square pattern with tiny black-and-white squares containing data that a computer system scans to read. This sort of bar code was created in 1994 by Denso Wave—a branch of Denso, a carmaker Toyota Motor Corporation unit—to track vehicle parts throughout the assembling process. In advertising, we use QR Codes to encode the URL of a website with coupons or product information.

You can see them on concert and sports event tickets. Mobile phone cameras or laser scanners read patterns. Then, special software decodes them. The finder pattern in the three corners of the QR Code tells a scanning device how to orient the code. In contrast, the alignment pattern and timing pattern—row and column of alternating black and white squares connecting the large squares of the finder pattern—are used to check for distortions and serve as the QR Code’s coordinate system.

Optical Character Recognition (OCR)

Optical Character Recognition (OCR) transforms scanned images of text, camera photos, screenshots, etc., into editable electronic documents. This technology eliminates the need for manual data entry, thereby saving time and energy and optimizing productivity. OCR systems function through the integration of both hardware and software. Digital libraries and PDF document editors are among the applications that rely on image recognition. Some other common uses of pattern recognition include automated security systems, facial recognition, and automatic speech recognition.

What use does pattern recognition serve? 

The goal of pattern recognition is the concept that human decision-making is connected to pattern recognition. For example, the next move in chess is determined by the current pattern on the board, and a complex algorithm decides to purchase or sell stocks based on financial data. Pattern recognition is employed to streamline these complex mechanics of decision-making and automate these various tasks.


Pattern recognition in computer science is a powerful tool for interpreting and recognizing data. It has numerous applications, including image processing, machine learning, data mining, and more. It is a complicated process of recognizing patterns in data and providing valuable insights. Its capability to identify and classify patterns enables us to comprehend our data better and make better choices. Pattern recognition can help us uncover trends in our data to make predictions and refine our decision-making. Using pattern recognition, we can make better-informed decisions to help us reach our objectives.

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