The convergence of digital transformation and nano-optical technologies has accelerated recently, leading to a surge in imaging (image and optical) research. Despite this trend, quantitative analyses surveying domestic research trajectories are rare in South Korea. This study addresses this gap by performing text mining on 167 abstracts published in the Journal of the Korean Society of Imaging Science and Technology over an 11-year period (2014–2024) to quantitatively understand the flow of domestic imaging research. The analysis involved several steps: keyword preprocessing (including stop word removal and lemmatization), keyword frequency analysis, LDA topic modeling, and semantic network analysis. The research primarily identified six distinct topics: optical coating and performance optimization, development of nano-material-based optical devices, optical property measurement and analysis technology, image processing and display technology, organic polymer-based new materials and reaction studies, and functional materials and energy applications. The relationships between core keywords within each topic were further examined through semantic network analysis based on co-occurrence frequency. This study contributes to future research roadmap development by providing data-driven insights into the specific research axes and growth areas within the domestic imaging field.