In our daily lives, pattern recognition goes by different names. But whether it’s problem-solving, trouble-shooting, or issue spotting, the main point stays intact. Patterns make our lives easier and our problems less intricate.
If we shift our focus from everyday challenges to business operations, we’ll most likely see pattern recognition suffused across all corporate activities. This core technology is the lifeblood of big data analytics since it distills valuable data bits and taps into the very heart of information.
Therefore, if your company lags behind the competition, it’s high time to avail of pattern recognition to prompt your business towards continuous improvement and evolution.
That’s why we’re here. Let’s sink our teeth into this astonishing technology and its real-life use cases.
Under The Hood Of Pattern Recognition
Pattern identification is essentially a branch of cybernetics that focuses on the theoretical foundations and methods of classification and identification of objects that share a set of properties and attributes.The technology distinguishes and classifies data using special algorithms.
Put simply, pattern identification is geared towards recognizing patterns and regularities in sets of information. And a pattern is a notion that repeats regularly based on a set rule or condition.
The data itself can encompass a wide range of phenomena:
- Employee names
- Multimedia, etc.
Due to its universal concept, pattern recognition is one of the four pillars of Computer Science. It tracks down the similarities among small problems that can help us address more complex issues more effectively.
Pattern Recognition Models
According to ResearchGate, the technology includes the following methods:
- Statistical Techniques – used to identify the features that put objects into specific groups.
- Structural Techniques – applies a hierarchical perspective to identify more complex patterns and relationships between objects (like parts of speech).
- Template Matching – pinpoints the similarity between two items of the same type ( such as shapes, curves, etc).
- Neural Network Approach – mathematically based assessment of complex interrelationships within groups (it mimics the way our brain works).
- Fuzzy Model – centers on recognizing and interpreting data that is vague and lacks certainty.
- Hybrid Models – applies a combination of all methods.
So far, this introduction seems far-fetched to the uninitiated people, right? Then let us explain the use of pattern recognition software in the real world.
Real-life Applications for Pattern Identification
There is a fine line between pattern identification and data analytics. So fine that those two technologies are often used interchangeably.
The main application field of pattern identification in data analytics is its ability to describe data and pull out its unique characteristics, thus making the whole understanding of data more panoramic. Pattern identification has found wide application in equity market forecasting and customer research. Have you ever heard of Google Analytics? That’s what we’re talking about.
Natural Language Processing
Natural language processing or NLP is a field of knowledge that inherits the best of computer science, artificial intelligence, and linguistics. Its main goal is to process and comprehend natural language in order to translate text and answer questions. While it may sound like the rise of the machines, there’s nothing magical about it. It doesn’t infer anything from the context. Instead, it processes clear and explicit messages.
With the rise of voice interfaces and chatbots, NLP has become one of the most important artificial intelligence technologies. But fully understanding and reproducing the meaning of language is an extremely difficult task, as human language isn’t easily mastered. That’s when pattern recognition kicks in. Although it doesn’t help cut to the chase, it empowers NLP to parse, segment and build a model upon which the proceedings are managed.
Excellent examples of NLP include:
- Email filters. Email filtering analyzes incoming emails for red flags. Inbound email filtering scans and groups messages into different categories such as spam, important, social media, and others. Outbound email filtering also searches the emails for any potentially harmful content. Companies can make use of this technology using a cloud service or on-premises appliance.
- Online search engines. In particular, we’re referencing semantic search. Natural language processing has the capacity to train a language model on a large corpus of documents, thus segmenting search queries based on their semantic So instead of surfacing search results based on exact keywords, search engines target the content. Google’s BERT update, for example, employs NLP to a full body of text, i.e. a Google search, and understands the intent behind the query.
- Predictive text and autocorrect. Have you ever noticed how Google Docs or iPhone finishes the phrase for you? That’s how predictive text works. In this case, NLP and pattern recognition identify and suggest words the end-user may wish to put in a text field. Autocorrect also benefits from both technologies to correct misspellings as you type.
- Plagiarism checking. NLP technologies are also at the heart of plagiarism checkers to detect matches in texts. With the help of semantic analysis, plagiarism checkers break down the text into constituent segments, and then the program analyzes each segment to search for matches on the Internet. One of the most popular examples is Copyscape and ContentWatch.
Other applications of Natural Language Processing include chatbots, machine translation, text generation, and others.
Image Pattern Recognition
Image recognition is another subtype of Artificial Intelligence and Deep Learning. The technology leans on large-sized data, such as images. Therefore, its single data point involves lots of information. Essentially image recognition is applied to obtain, understand, process and analyze photos from the real world, with further conversion into digital form.
Although being widely used, this technology still doesn’t replicate the human brain. So instead of “recognizing”, it characterizes the image so that it’s suitable for further search and comparison. The main algorithms that underpin this technology are a combo of unsupervised and supervised ML algorithms.
There are lots of use cases for Image Recognition, including:
- Stock photography. Leading websites have already embedded image recognition into the field of stock photography and video to tag keywords faster and more efficiently. Since stock photo sites abound in visual material, tagging thousands of photos per day requires much time and effort. Also, the content won’t be indexed and be accessible to users without proper keyword attribution. Image recognition facilitates searches by automatically suggesting keywords that are relevant to the image.
- Visual Search. This functionality is widely used for improved product discoverability in the ecommerce industry. Visual Search enables customers to look for similar images or products using a reference image. Also, visual search features have found broad applications in search engines such as Google Search. Search systems use the searcher’s image to locate a comparable ‘twin’. This is done by stamping the image with keywords, so the search engine can identify the contents and find an image with the same keywords.
- Face Recognition.This technology identifies faces in a photo or video content by comparing it with a pre-existing database.The comparison can be done via servers, IP cameras, or access control systems.You can see face recognition in social network services, Face IDs, or attendance systems.
Sound is on par with other essential sources of information. For a long time, there was no effective recognition model that could classify the sound. Today, recognition algorithms are augmented by language models that describe the structure of a language. Also, the system uses real speech material for training.
Popular application areas of voice and sound recognition include:
- Virtual assistants. This is one of the most common uses of voice recognition software. Google assistants and smart speakers use NLP model processing to formulate the message and sound samples to verbalize it.
- Digital transcription. This one has turned out to be especially helpful in the field of healthcare. Currently, speech-to-text software is widely used at doctor’s appointments to simplify storing, organizing, and accessing data in patients’ medical records.
- Automatic Caption. Captions make content more visible for users. Thus, YouTube applies voice recognition to automatically create subtitles for videos.
Sentiment Analysis is a subtype of pattern identification that ascertains the attitude of the author to a certain topic. Put simply, it goes as far as reading between the lines, i.e. goes beyond words and all the way to emotions. It sounds like the most advanced technology and it truly is.
Sentiment analysis has grown to become a viable business tool that can be used to fetch user feedback from different social media platforms and other digital sources. For that, the system uses unsupervised ML algorithms mixed with the basic recognition procedure.
Its main application areas include:
- Recommender systems seek to predict a preference that a user will like. The recommendation may be backed up by queries and the user’s history. You probably see those every day on Netflix ( ‘you might also like’) or on some online marketplace (‘people also buy’).
- Brand monitoring. Companies always need to keep tabs on their brand image. Sentiment analysis helps them track different channels to identify how their brand is mentioned. This also means that companies can collect valuable customer feedback for further improvement.
- Customer segmentation. Sentiment analysis can also be used to identify which customers feel negative about a company and address those issues. Likewise, this technology can help business owners pinpoint the most loyal customers.
The Bottom Line
Pattern recognition is the key to doing everything right since our life consists of billions of repeated models. This technology also prompts faster tech growth and opens up a plethora of business opportunities. With the help of pattern recognition, data analytics can grow exponentially and at a quicker pace.
Our article has demonstrated the large-scale use of this technology across industries. Therefore, it’s high time to amplify your business operations and tap into the tech comforts we have today.