Patterns are at the heart of everything we do. From the natural world to the math curriculum, they provide an insight into the underlying order and underpin our understanding of the world.
A deep understanding of patterns unlocks a whole host of opportunities where this knowledge can be applied. In particular, the explosion in ‘big data’ has rendered the field of pattern recognition crucial to business operations and provided unprecedented veracity of incoming information.
With that in mind, our article will look under the hood of pattern recognition and spill top use cases of pattern recognition software in the business world.
What Is Pattern Recognition?
Let’s grapple with the basics first. The ability to recognize relies on the ‘sameness’ of similar or identical objects. Although all phenomena are unique per se, they all belong to groups based on their similarity. Therefore, the notion of pattern identification refers to segmenting an entire set of objects into non-intersecting subsets where all the elements have similar properties distinguishing them from the elements of other groups. And, thus, the task of recognition is to distinguish and segment data based on the pre-defined criteria or patterns.
Pattern recognition encompasses all kinds of information, including:
- Video and audio content
- Emotions, etc.
Since pattern recognition is the backbone of learning that has the capacity to enhance computer algorithms, it is one of the pillars of machine learning.
Focal area of pattern recognition
Apart from specific tasks, a pattern detection system should be able to do the following:
- Find a known pattern fast and efficiently.
- Group unknown phenomena
- Identify shapes and objects from various perspectives.
- Detect patterns even non-obvious
- Automatically identify regularities
Now let’s have a closer look at the process of training the system to find patterns.
How To Train A Pattern Recognition System
To set up a pattern identification system, you need to decide on a technique and divide data into sets. As for the models, classification and clustering algorithms are the go-to models used for pattern recognition.
Data is typically divided into three sets: training, validating, and testing. As the name suggests, training data helps to train the model, validating data assists in fine-tuning it, whereas testing data is used to test the chosen technique.
Overall, the lion’s share of data becomes a training set. Validation and training sets rely on the 20% of remaining input.
Applications of Pattern Recognition
What separates serially good deciders from the rest is their aptitude to glean more profound operational insights. With the advent of big data and machine learning, pattern identification suffused industries and application areas to increase our decision-making speed and accuracy. Let’s see how broad its area of implementation is.
Natural Language Processing
Natural language processing or NLP is a subtype of pattern recognition that operates at the intersection of machine learning and mathematical linguistics. This field of knowledge helps computers decipher, comprehend, and process human language.
As a result, machines are able to evaluate sentiment and segment the most important pieces of our speech. And since computers mostly communicate in zeros and ones, natural language processing is an extremely intricate technique that’s sometimes messy and unstructured.
Anyway, NLP spans a gamut of industries and directions, including:
- Spell check – a popular NLP feature widely used in operating systems and applications that detects spelling errors;
- Plagiarism detection – a type of text analysis that scans through a body of text to see if the text has duplicate content. A textbook example of plagiarism checkers is Copyscape;
- Search autocomplete – another face of NLP that we all use on a daily basis. This feature finishes the search for users based on a collection of the most popular search queries. You can see it on Google search’
- Text generation – this Natural Language Processing branch leverages computational linguistics and AI to automatically produce content. It empowers chatbots and AI assistants;
- Machine translation – an automated translation performed by a computer that relies on a specific set of algorithms and sentiment analysis.
Voice-enabled applications have become increasingly common in our daily lives. Just a few decades ago, a computer understanding a human language was more on the sci-fi side than reality.
But today, automatic speech recognition and NLP are what empower this advancement. First, it was hidden Markov models (HMM) that decoded our speech. Currently, more efficient methods such as deep neural networks are leveraged to ensure automatic speech recognition systems with an accuracy of 81%.
Voice and sound recognition are broadly used in the following realms:
- Virtual Assistants apps use voice recognition to perform tasks for users based on commands or questions. For example, Siri, Apple’s voice-controlled personal assistant, responds to voice commands thanks to pattern recognition.
- Acoustics-based machine fault diagnosis uses principal component analysis and artificial neural networks to analyze the sound of the operating machine. It then helps spot anomalies and suggests a source and ways of fixing them.
- Speech-to-text and reverse transformation uses a comparative database of samples to accurately convert speech into text and vice versa. You can see this feature on Medium.
- Automatic caption addition automatically transcribes the video and uses image overlay to demonstrate the subs on the screen. These captions are produced by ML algorithms, hence the quality of the captions may vary based on the language and articulation.
Optical Character Recognition
Optical character recognition or OCR is the process of automatically distinguishing and converting handwritten or written texts into computer-readable text data. The most common data sources for OCR are printed paper documents and photographs. After a scanned paper document is processed, you can edit it in one of the word processors like Microsoft Word or Google Docs.
To adapt the written text, the OCR technology leans on a library of patterns and pits them against available input documents. Any found matches then go through a language corpus to present the content in a code-enabled format.
The most prominent use cases of Optical Character Recognition include text transcription, handwriting recognition, and document classification.
Image Pattern Recognition
Image recognition is a subcategory of computer vision that uses pattern identification to detect and analyze images for specific tasks. One of the most challenging and promising areas is image processing and recognition that completely mimics human vision. However, today, image recognition is mainly used to classify images in social media or photo stocks. Therefore, it helps companies to deliver better and smarter services to the users accessing it.
Besides, this technology is the lifeblood of the following areas:
- Visual search features use real-world images as an incentive for digital searches. Search Engines and eCommerce marketplaces leverage this tech comfort to facilitate the product exploration and enhance personalized customer experience.
- Face detection is a computer technology generally utilized in social network services like Instagram and law enforcement. It finds and identifies human faces in digital images. The technical process usually begins with searching for eyes and then detects other facial regions including eyebrows, mouth, nose, and others. The computer application also compares the face to images in a database of stored records.
Last, but not least is the interpretation and classification of emotions also known as sentiment analysis. It’s the contextual mining of texts that combs through the words to identify the author’s emotions and intent behind letters.
Sentiment analysis tools usually group pieces of writings into negative, positive, and neutral classifications. Why would anyone do that? Well, modern sentiment analysis software can handle vast quantities of customer feedback to uncover customer’s opinions about brands, products, or services.
The real-life applications of sentiment analysis include:
- Audience research – performed by companies to segment their target audience, monitor interactions with brands, and optimize the content. Einstein by Salesforce uses AI capabilities to help users get smarter insights from their data.
- Service support – monitors incoming requests to define the nature of the query (positive/negative). This helps companies to track customer success and align with customer’s voice.
- Product recommendation – used to suggest the item of interest for the particular user based on previous product reviews. The best examples are Amazon with their “customers who bought this item also bought” and Spotify with “discover weekly”.
The Final Word
Pattern recognition is paving the way for further maturation of computational thinking. Although it hasn’t hit full throttle yet, this technology already prompts big data analytics and equips companies with actionable insights and accurate data.
Therefore, if your organization hasn’t tapped into pattern detection yet, it’s high time to jump on this train and avail of ML potential.