Analysis of Pattern Recognition Methods in Digital Image Processing: A Review of Recent Literature

Abstract

This research collects data from an important part of digital image processing.The objective of this research is to discover and classify objects or visual features based on specific characteristics.Various methods have been developed in recent decades.This includes statistical methods and feature-based manual methods, but also contemporary techniques such as machine learning and deep learning. This research is a recent literature review that examines various pattern recognition techniques in digital image processing.By using systematic review techniques on scientific articles published in the last five years, this research examines the advantages, disadvantages, and development trends of each of these techniques.The research results show that deep learning methods—particularly convolutional neural networks (CNNs)—are the most studied today due to their ability to automatically extract features and provide high accuracy. This study provides researchers and experts with a comprehensive understanding of how to choose the appropriate pattern recognition method for the needs and characteristics of the images being processed.Keywords: Pattern Recognition, Digital Image Processing, Literature Review, Deep Learning, Machine Learning, CNN, Object Detection