The problem for the localization, detection, and separation of any LCD panel defects is nontrivial and crucial for the LCD manufacturing industry. This study presents an automatic generation method of laser cutting paths for LCD panel defects and introduces a novel color normalization singular value decomposition Reinhard neutrosophic Canny ( $\mathrm {SVDRN}\mathbb {C}$ ) segmentation framework. This includes developing a unique ( $\mathrm {SVDRN}\mathbb {C}$ ) method to overcome the problem of color variation in images that occurs during LCD panel image acquisition. Then, the neutrosophic Canny edge segmentation method is proposed to identify the edges of the segmented image. In addition, the end coordinates of an edge-detected image at the top and bottom directions are determined, allowing for the generation of straight lines to guide laser cutting. Finally, the experimental evaluations on 250 industrial TFT-LCD panel images are performed where the proposed method outperformed the other eight methods, registering a higher peak signal-to-noise ratio (19.21), Dice score (0.9), Jaccard similarity (0.92), and correlation coefficient (0.91) metric values. Therefore, the $\mathrm {SVDRN}\mathbb {C}$ method can help manufacturers reduce LCD panel yield losses.