What is Pattern Recognition?
A pattern is basically a trend of data, forms, design, shapes, lines, colors or a mixture of these. A singular part of the pattern is known as the motif, which may be placed regularly or irregularly to form a pattern named respectively. Human beings have in-built mechanism to recognize patterns by matching input image, its features and patterns with the information that is pre-stored in the brain as a memory. This process is commonly known as pattern recognition in humans that requires connection building between perceived information and the pattern-based memory to trigger the innate ability of identifying structured patterns in the environment, originating from the outermost encephalon layer, neo-cortex.
Recognizing patterns of interest by machines is a maneuver considered in various fields, for example, in recognition of images of fingerprints, bar or QR codes, faces (refer to our blog on face recognition using opencv to know more), voices, speeches, ultrasonic and other sound waves, tree species or handwriting. Artificial Intelligence pattern recognition and classification can be supervised, where a given labelled pattern belongs to a predefined category in the database or unsupervised, where the system learns to classify an un-labelled pattern data and assigns it to an undefined group based on previously encountered pattern similarities or attributes. It does so by cleaning noise, mapping, extracting important features and labeling information obtained from the provided input data over time. This helps in automating day-to-day repetitive decision-making processes such as stock investments, chess movements etc.
The above diagram showcases pattern recognition in medical imaging
Pattern Recognition and AI/ML
Being a subfield, artificial intelligence and pattern recognition are like the two sides of the same coin as the latter helps the machine gain human-like intelligence for performing mimicked actions in case of certain tasks like object and facial recognition. Patterns are used for training Machine Learning models to perform pattern recognition of unique data, during statistical data analysis like speech, waveforms and textual pattern recognition or image data analysis as in movement, medical scans, video and facial pattern analyses. Some of the common ML algorithms utilized for pattern recognition are:
- Support Vector Machines: A supervised learning algorithm, abbreviated as SVM, is used for artificial intelligence pattern recognition via image classification and regression. It works by creating high-dimensional hyperplanes that separate data points of different classes and identifying the most efficient amongst them.
- k-Nearest Neighbors: It is a non-parametric algorithm used for artificial intelligence and pattern recognition-based classification and regression analysis. K-NN identifies the nearest neighbors of a given input data point in the feature space and classifies it according to its similarities with a major class.
- Decision Trees: Similar to pattern recognition in humans, this algorithm serves purposes like SVM and k-NN. It is composed of a tree-like structure, where end-leaf nodes represent a class or category. The branches are traversed based on the decisions taken in the interconnected internal nodes representing certain features respectively.
- Random Forest: This is defined by multiple decision trees combining as a part of an ensemble learning method during training for image classification (combinatorial output of class mode) and regression (mean prediction).
- Hidden Markov Models: HMMs, applications of reinforcement learning, are utilized in high pattern recognition such as image, sequence recognition and analysis tasks (Markov processes involving random procedures indexed by time) by forming a sequenced set of states featured with observational probabilities.
- Principal Component Analysis: The original features of the input data are transformed into sets of uncorrelated variables or principal components with original data variances by PCA models. This is useful for dimensionality reduction in feature extraction.
- Gaussian Mixture Models: Useful in artificial intelligence pattern-based solutions such as density estimation during clustering, GMM models distribute complex input data into a Gaussian distribution mixture.
How does Pattern Recognition Work?
Approaches
Given below are a few methods of pattern recognition. To gain detailed insights on mathematical models used in various pattern recognition systems, you may go through an e-book named Pattern Recognition and Machine Learning by Christopher Bishop.
- Statistical Pattern Recognition: Statistical Pattern Recognition, also known as decision-theoretic pattern recognition or StatPR, follows a traditional approach. In this method, high-dimensional pattern recognition involves extracting features and modeling them based on the number of features in 3D, 4D, and so on, up to a d-dimensional feature space. Each pattern vector is assigned a specific space in the d-dimensional realm without overlapping with others, forming distinct clusters. Through this process, the machine encounters numerous patterns, learning to process, recognize, and classify them during training.
- Syntactic Pattern Recognition: In this method, interaction of sub-patterns such as an alphabet, that form a whole pattern, such as grammatical corrections in sentences, are considered. The SyntPR is used to resolve complex problems involving texture images and EKG waveforms by recognizing and describing the sub-pattern assembly structure captured within a predefined set of rules. Due to huge permutational and combinational possibilities, large training datasets may be needed, therefore requiring more computational power.
- Template Matching: In this type of model, a reference template is used to compare and find similarities in each sample data of the same type. For example, sections of an input image are compared to the template in the case of medical digital image processing, facial recognition, path planning, and quality control in manufacturing industries.
- NLP-based Pattern Recognition: Natural Language Processing is used by machines to understand, analyze, interpret, recognize, translate and classify human text and its patterns. An example of this is evident in applications like Google Translate
- NN-based Pattern Recognition: Neural networks are composed of layers of processors interconnected with subunits called neurons that perform decision-making on spatial hierarchical visual data features for image and object recognition tasks, all the while, altering weights during iterations. To know more on deep CNN, the most common neural network, do check our blog on types of neural networks.
- Hybrid Pattern Recognition: The most efficient pattern detectors are a combination of two or more lighter PR algorithms that may vary as per pre-processed input data type, application and performance requirement.
The above diagram showcases types of pattern recognition systems
Pattern Recognition Systems – Design Principles
AI pattern recognition involves digital data acquisition, preprocessing for removing noise, extracting and examining relevant features of patterns, data representation involving placement of features in d-dimension that is grouping elements into segments, post-processing for improving accuracy and ultimately, decision making based on analyzed data insights. One needs to carefully consider certain principles such as modular designs that allow easy integration of algorithms as well as components, given accuracy, performance of algorithms are increasing annually whereas time of development of AI/ML innovations, time of obsolescence, go-to-market has decreased drastically. Another principle that needs to be paid attention to is the selection of learning techniques amongst supervised, semi-supervised, reinforcement and unsupervised learning, which aids in reducing errors during training.
Pattern Recognition Systems – Applications
Optical Character Recognition
Commonly known as OCR, it is used for classifying optical patterns present in a captured image led by initial image segmentation and feature extraction. Learn more about how OCR works better than traditional manual work in our blog about ocr receipt scanners. An OCR device scans and captures the physical or e-receipt data as an image file, converts it into machine readable text in the software, then recognizes, extracts and classifies individual characters of the document in the form of key-value pair extraction automatically within seconds.
The OCR technique helps in dealing with millions of receipts, invoices and other document hard copies by digitizing them for easier access and management. With the help of OCR, these documents can be made accessible across geographies, with minimal human errors, labor cum operational costs and turnaround timeframe. It avoids customer dissatisfaction, especially around product royalties as well as fraudulent issues of fake receipts. Furthermore, most OCR solution providers ensure that this technology is powered by artificial intelligence pattern to form Intelligent Document Processing offering certain extensive features such as supporting variety of input data, layout detection, table parser, document localization and correction, text restoration, shadow removal, synthetic docs generation etc. This is also useful for autonomous vehicles for detecting traffic signs etc.
Facial Recognition
Visual search image and face recognition techniques detect facial patterns and objects in images or videos section-wise through scanning firmware, where labelling of image segments and classification is powered by deep neural networks for AI pattern recognition-based recommendations on identifying human faces through nodal point measurements in real-time. This is very useful technique and has various applications such as for device authentication, patient management and hospital data privacy, law enforcement, event surveillance, geo-fencing in VIP areas and border security, quality control and defect inspection, among others. It helps retailers gain real-time insights on customer sentiment analysis via facial signatures and provide them with a personalized shopping experience, meanwhile, ensuring theft, crime and infringement prevention through secured access management and on-premise security.
TRAZER
The usual license plate recognition systems are used to capture, process and enhance the quality of license plate images from passing-by vehicles. The captured pictures are passed on to the firmware that consists of OCR-based pattern identification algorithms for further interpretation. Traffic Analyzer and Enumerator or TRAZER is our proprietary product used for real-time video processing and vehicle detecting, classification and tracking system. The model is trained using traffic videos and feature analysis and computations. Know more on how we helped SMBs in achieving the same in our blog that talks about license plate recognition systems. TRAZER has various use cases in today’s world such as for toll security, transportation and fleet management, law enforcement, premise surveillance, border security, parking assistance, drive-thru order management, traffic congestion reporting etc.
The above diagram shows LPR, vehicle identification & tracking display in TRAZER software
Others
AI pattern recognition is also used in stock market prediction such as predicting future value of a company’s stock and traded assets using deep learning models. Textual recognition is a form of ML-based pattern recognition as discussed in SyntPR section, that is used to translate, analyze, classify documents and even generate synthetic text by understanding human language. This is very useful in insurance fraud detection. Another use case where pattern recognition is utilized is handwriting and signature recognition involving complex shapes and irregularities.
Choose KritiKal for your PR Needs
In its 21+ years of experience, KritiKal has catered to various SMBs and Fortune500 companies in delivering pattern recognition and machine learning–based projects as per their requirement. Few notable use cases that can be mentioned in this case include vision subsystems for MAVs for horizon detection to compute the vehicle’s pitch and roll, authenticating 3D printed labels using mobile app based on Local Binary Pattern and SVM, bird flock detection and tracking for agricultural purpose, evaluation of sebum content during biophysical parameter mapping with respect to interpolated measurement pattern, anti-theft systems that raise alarms against irregularities in vehicle operational pattern, customer behavior analysis, wine quality chemical analysis and much more. Please call us or mail us at sales@kritikalsolutions.com to avail our pattern recognition-based development services and stand apart in the competitive market.
Pratham Dhawan holds the position of Senior Software Engineer at KritiKal Solutions. He is a certified Python-based convolutional neural network development professional, and he is also proficient in advanced C++, AI applications etc. He has contributed significantly to the successful delivery of various projects by KritiKal using his problem-solving skills and commitment to provide high-quality outputs throughout the development process.