With the rise of big data and machine learning technologies, pattern recognition is widely used across multiple industries. It is present at the core of every big data analytical tool and uses machine learning to provide organizations with a strategic advantage in today’s overly competitive business environment.
In simple terms, pattern recognition is defined as the process of identifying patterns through machine learning algorithms. It classifies data based on statistical information that’s been extracted from patterns or through knowledge that’s already been gained beforehand. Typically, its applications process raw data and convert it into a machine-usable form. It does so by classifying (for supervised learning) and clustering (for unsupervised learning) patterns.
Pattern recognition has immense application potential, which is why you can find them in your day-to-day life. In this article, we will discuss the various ways in which pattern recognition can be used.
Uses of Pattern Recognition
- Natural Language Processing (NLP)
NLP is a machine learning field that helps machines understand human language and interpret its messages. It uses several pattern recognition algorithms to build robust systems that can be used for text analysis, plagiarism detection, text summarization, text generation, contextual extraction, text translation, and text correction and adaptation.
- Network Intrusion Detection
Network intrusion is a grave threat to every firm that manages data. Pattern recognition applications help organizations detect network intrusion by identifying patterns over time. It keeps security systems on alert for even the slightest pattern change. It also enables organizations to approach network intrusion proactively by highlighting the loopholes that have made them vulnerable in the past.
- Image Pattern Recognition
Image processing and its segmentation is one of the leading, and rapidly growing pattern recognition uses. It combines the supervised and unsupervised ML algorithms for image recognition. First, supervised algorithms are used to train models on labeled datasets. It is then followed by unsupervised algorithms exploring the input image. Then, the supervised algorithms again kick in to classify the patterns according to the given categories. Image pattern recognition is mainly used for visual search (for example, in eCommerce marketplaces and search engines) and face detection (for example, in social network services and law enforcement). However, it is a rapidly growing field for which we can expect to see multiple other uses in the near future.
- Disease Recognition and Categorization
With the help of image pattern recognition, machines can also help in disease recognition and categorization. Also, pattern recognition systems can effectively analyze patient data in real-time to identify any anomalies so that quick action can be taken.
- Optical Character Recognition (OCR)
The analysis of images considered as alphanumeric text and their subsequent conversion into machine-encoded text is what OCR is all about. The OCR algorithm is based on the application and comparison of various patterns with input documents as available. It combines pattern recognition with comparative algorithms attached to the reference databases. Some common uses of optical character recognition are text transcription, document classification, and handwriting recognition.
- Speech/Voice Recognition
Pattern recognition systems combined with linguistics result in one of the most successful pattern recognition applications ever – Speech/Voice Recognition. Essentially, speech/voice recognition functions on the same principles as optical character recognition. Hence, it can be used for AI assistants, speech-to-text and text-to-speech software, automated captions, sound-based diagnosis of anomalies, and personal assistant apps.
- Agriculture and Animal Breeding Enhancement
Pattern recognition schemes help researchers identify, improve, and breed key animal traits. It helps in the enhancement of animal husbandry and thus contributes to economic benefits in the agriculture industry. Researchers can help increase the resistance to diseases, reduce environmental threats from fertilizers (by identifying the most harmful elements to facilitate action), and improve overall operations in the agriculture and animal husbandry industries with the help of pattern recognition.
- Financial Services
Pattern recognition systems can help the financial services industry in three ways:
- Identifying upcoming trends based on data so that firms can gain a competitive edge.
- Retrieving critical insights from data so that firms can identify their loopholes, understand patterns that continually lead them to failure, and offer better services to clients.
- Enhancing security and proactively approaching cyber threats to ensure timely detection of threats for quick action.
Apart from these three uses, there are several other ways that pattern recognition can be useful for the financial services industry, such as fingerprint identification, handwriting recognition, network intrusion detection, etc.
Today, human workforces are exceptionally talented and equipped with skills one could have never thought of earlier. However, even with immense talent, certain tasks will always be out of reach for human capabilities. Accurate and quick pattern recognition is one of them.
Pattern recognition systems that combine machine learning algorithms with pattern identification are more efficient, accurate, and quick than humans. One can rely on pattern recognition systems to offer highly accurate insights. These insights help organizations function more optimally, improve their business operations, and boost overall organizational performance.
Although all the uses mentioned above put forth how extensively pattern recognition can be applied, it is crucial to understand that every pattern recognition solution may not provide the same level of applicability, effectiveness, and accuracy. Hence, you must consider partnering with an experienced service provider to implement pattern recognition solutions in your organization. Having a detailed knowledge of the kind of solution you want and then partnering with the right firm can help you successfully leverage the advantages that pattern recognition has to offer.