With so many digital images coming online, it is not surprising that computer vision techniques are now being applied to specimen images. These techniques include segmentation \citep{allan_novel_2019,goeau_new_2020,white_generating_2020,de_la_hidalga_cross-validation_2022}, object detection \citep{champ_instance_2020,ott_ginjinn_2020,triki_objects_2020} and object recognition specifically to identify taxa \citep{carranza-rojas_going_2017,earl_discovering_2019,valan_automated_2019,little_algorithm_2020}. In recent years, machine learning in particular has become mainstream and has been built into workflows that start with digital images and their metadata and result in statements about the image and what it illustrates. Such workflows can be used to extract information about a biological specimen from the typed or handwritten text on them \citep{allan_novel_2019}. Yet, there are many other uses for image analysis of specimens as we elaborate on below \citep{pearson_machine_2020,soltis_plants_2020}.