This paper presents EGGS, a novel and purpose-driven image processing approach designed specifically for creating a standardized and comprehensive dataset of avian egg images. The proposed approach integrates advanced techniques for image preprocessing, orientation detection, and the removal of damaged specimens, addressing key challenges in oological data analysis. With a primary focus on enhancing the quality of avian egg datasets, this study highlight the potential for combining advanced image processing techniques with deep learning models to address challenges in ecological data analysis. Beyond basic preprocessing, EGGS incorporates a robust orientation detection component and precise contour analysis, offering a focused and efficient framework for egg annotation. This innovative solution is tailored to support targeted ecological and heritage research, providing a unique tool for advancing ornithological studies. The resulting dataset not only enables downstream analyses but also serves as a critical resource for ornithological research, opening pathways for deeper insights into avian reproductive strategies, evolutionary adaptations, and environmental resilience.