Fisheries management is generally based on age structure models. Thus, fish ageing data are collected by experts who analyze and interpret calcified structures (scales, vertebrae, fin rays, otoliths, etc.) according to a visual process. The otolith, in the inner ear of the fish, is the most commonly used calcified structure because it is metabolically inert and historically one of the first proxies developed. It contains information throughout the whole life of the fish and provides age structure data for stock assessments of all commercial species. The traditional human reading method to determine age is very time-consuming. Automated image analysis can be a low-cost alternative method, however, the first step is the transformation of routinely taken otolith images into standardized images within a database to apply machine learning techniques on the ageing data. Otolith shape, resulting from the synthesis of genetic heritage and environmental effects, is a useful tool to identify stock units, therefore a database of standardized images could be used for this aim. Using the routinely measured otolith data of plaice (Pleuronectes platessa Linnaeus, 1758) and striped red mullet (Mullus surmuletus Linnaeus, 1758) in the eastern English Channel and north-east Arctic cod (Gadus morhua Linnaeus, 1758), a greyscale images matrix was generated from the raw images in different formats. Contour detection was then applied to identify broken otoliths, the orientation of each otolith, and the number of otoliths per image. To finalize this standardization process, all images were resized and binarized. Several mathematical morphology tools were developed from these new images to align and to orient the images, placing the otoliths in the same layout for each image. For this study, we used three databases from two different laboratories using three species (cod, plaice and striped red mullet). This method was approved to these three species and could be applied for others species for age determination and stock identification.