Nowadays, clear digital images of wild flora and fauna are readily available because of which the research communities are showing interest in in-situ measurements. However, object orientation is the primary constraint in estimating size and shape. It is often sighted in fossil specimens or natural history collections that produce shape deformation and distortions from their actual size and shape. We conducted an empirical evaluation with Cardboard Model Fishes to determine the shape variations due to digitization error and object orientation. Our results show that (a) Object-orientation creates Procrustes shape variations and (b) Generalized Procrustes Superimposition (using least square fit) reduces non-shape variations and the issues of Object-orientation. In the Procrustes Generalized Least Square optimization method, the variance gets distributed across landmarks that minimize the differences due to Object-orientation up to ≤ 20° away from the image plane. These minor corrections by the Procrustes method assume significance that aids in defining filter criteria for the profitable usage of digital images with Object-orientation issues taken under field conditions and in the augmentation of digital images in Machine Learning. Our results show that transfer learning (in silico) is more efficient than Procrustes detecting subtle shape variations. Our evaluation would encourage others to derive the maximum benefits of digital images acquired from Webcam, Digital cameras, Camera traps, Unmanned Vehicles, and broader applications in e-commerce platforms.