Through feature extraction and pattern analysis, the suggested research study offers an object identification system that enables human perception-based object recognition. Each subsection is treated using both approaches. Shape is used for feature extraction, and statistical bins are made for each vehicle pattern for analysis. Following analysis, a weight is assigned to each measure in order to help determine the degree of similarity or dissimilarity between each bin. Each bin comprises both variables. In computer vision, object recognition is a crucial job having applications in autonomous driving, robotics, medical image analysis, and many other fields. This article discusses important ideas, techniques, and current advancements as it investigates the idea of product analysis using benchmarks. The detection and categorization of characters from digital images is the focus of a significant body of research in design definitions called image character recognition (ICR). On the other hand, response tests, such as catches, are used by systems to determine whether a user is human. They have traditionally been thwarted by automated attacks such as negligent hacking and inappropriate capture utilization. ICR uses a method called pattern analysis to identify the characters in an image. This method of instruction called Shape analysis is used in design analysis to locate elements in digital images. Pattern analysis applied to capture discovery is examined in design analysis. This essay examines the difficulty of locating captures and provides a framework for analysing patterns.