The Printed Circuit Board (PCB) accommodates various Integrated Circuit (IC) components arranged in a specific layout of bond pads, lines, and tracks. Throughout the manufacturing process, irregularities or defects often occur during drilling, etching, stripping, and other stages, impacting the performance and functionality of the circuit board. Many of these defects are related to soldering pads and copper balance, identifying them through manual inspection is time-consuming and error-prone. This necessitates the use of Automated Optical Inspection (AOI). Practices like template matching often require two identical PCBs, which are compared using mathematical algorithms to detect differences. However, they are not resilient to viewpoint variations and non-rigid deformations. The current inspection process primarily focuses on rectifying PCB images captured with tilts ranging from 0 to 84 using homography principles. This correction process operates within a maximum run time of 7.96 s. The adjusted images then undergo analysis via a pattern-matching unit, where the system receives images of the same PCB pattern, each exhibiting different defects. Structural information mapping is performed using various spatial-domain feature-based matching algorithms. When evaluated using SSI and MSE metrics, the model achieved high matching percentages of 99.67%, 99.75%, and 99.30%, and low error rates of 0.343%, 0.358%, and 0.721% for three different types of PCB designs considered. Additionally, the model excels in precisely identifying the location of defects in the PCB images without using bounding boxes, in accordance with the description of the co-images through a segmentation approach. Overall, the proposed system effectively corrects skew, accurately detects abnormalities and outperforms traditional assessment systems.