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}.