A couple of decades ago, digital data carried little significance. While structured data collectively composed the relational database, the unstructured data barely made any sense to administrators. With the advent of data mining tools, the data engineers make effective use of unstructured data to make intelligent decisions. An entirely new research field of pattern recognition techniques emerged over the years.
Pattern recognition is the practice of identifying common features to find a meaning out of unstructured data. It enables business intelligence executives to make strategic decisions. The primary factor that distinguishes these techniques from traditional methods is the ability of software to self-teach it. The data engineers do not need to place data manually in relevant positions to get the insights. Instead, the algorithms are programmed to find the similarity in data.
Unstructured data belongs to a wide range of sources including text, audio, video, and images. The text defines alphanumeric characters as well as symbols. The classification of data requires the application of machine learning algorithms.
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Application of pattern recognition varies depending on the nature of data and intended outcomes.
Nevertheless, the field can be classified into following major types.
This is one of the most widely applied branches. It enables AI to classify the images depending on one or more features. Such software labels certain attributes to an image so that the searchers may find this picture with a set of similar pictures.
Since the image requires attributes for classification, supervised learning algorithms are appropriate for image recognition. Supervised learning requires adding bias to the classification process. Bias enables software to suggest the output in a fixed set of boundaries. The AI experts ensure that the results remain in limits instead of recommending something intensely unintended.
Face detection is the most used application of image recognition. iPhone X uses this software to recognize faces for unlocking the phone. Initially, it requires supervised learning. However, most of the recognition software systems are capable of self-learning.
The earlier face detection schemes could only detect the coordinates of a picture where the face was most likely located. Cameras often create “bounding boxes” around faces to define the focal point.
Further improvements in algorithms enabled the researchers to distinguish various facial features. For instance, the software creates encapsulations around nose, lips, and eyes. Since these features vary from person-to-person, the algorithms can be programmed to tell apart between each person that ever inhabited the earth. The quality of recognition depends on the intensity of software training. Face detection algorithms at Facebook are capable of predicting the person by considering the previous photos of user in subject.
Archeologists and anthropologists always feared about losing the transcripts of ancient and medieval periods. However, they do not need to fear anymore. Research in optical character recognition (OCR) enables the scientists to preserve the data in digital form without typing a word manually.
OCR algorithms recognize each character in a script and save the entire document in one of the types of text files. The document does not necessarily have to be typed – as with the case of ancient writings. Algorithms are also capable of recognizing handwritten scripts. However, the handwritten algorithms usually require more training since they do not follow fixed fonts – unless a professional calligrapher writes them.
One significant application is the identification of characters on the license plate. The city administrations in a number of metropolitan areas place cameras with character recognition capability. These cameras send back details of vehicle owners after recognizing the characters on plate. The administration may automate the process of penalizing the traffic violators.
Audio Recognition and NLP
If you are a frequent user of Siri, Google assistant, and Cortana, you must be grateful to the set of audio recognition software which powers them. The voice assistance in vehicles and smart homes also owe to research in advanced pattern recognition.
The recognition also enables the users to place captions and search queries by speaking instead of typing. They enable the music lovers to find the title a song all by using recorded audio. Apart from assistance over the internet, the algorithms allow the law enforcement agencies to recognize criminals.
Computers and computing languages lack the ability to process human languages. They understand in binary terms. Every high-level language requires binary conversion. Thus, Natural Language Processing (NLP) is a dedicated field to train computers in learning human languages.
The developments in Machines Learning enables AI engineers to capacitate computers for summarizing texts and understanding semantics. Although researchers are still in process to improve the results, yet the field already attained decent milestones. Word Processors and grammar correcting software systems are among the chief beneficiaries of NLP.
The availability of huge amount of data at their disposal is no longer a burden on businesses. They can use data to learn the customer behavior and define policies and strategies accordingly. Customers belong to varying demography. The differences in ages, socioeconomic statuses, origins, and genders allow the executives to tailor the strategy to fit consumer’s needs.
Pattern recognition softwaresystems enable data scientists to achieve these feats. Both Machine Learning and Deep Learning techniques are used for data analysis. Besides, they allow the companies to perform product research and find out the shortcomings of design and features ahead of the launch.
Google search engine presents one of the most impressive examples. It determines the choices of the user by considering the past searches and recommends the most relevant ones.
The AI Pattern recognition presents much more than this brief introduction. Companies can achieve wonders by using recognition algorithms. They can outperform the most competent rivals and lift business from ruins. The field is making its mark in various industries for over a decade now.
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