Fisheries management is generally based on age structure models. This fish ageing data for these models is collected by experts who analyze and interpret the surface of calcified structures (scales, vertebrae, fin rays, otoliths, etc.). The otolith, in the inner ear of the fish, is the main calcified structure used because it is metabolically inert and contains information throughout the whole life of the fish. Each year, nearly one million otoliths are collected worldwide, including 35 000 in France, to provide age data for all commercial species. This traditional method to determine age is therefore very time consuming. 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 on the ageing data. Otolith shape, resulting from the synthesis of genetic heritage and environmental effects, is a useful tool to identify populations, therefore a database of standardized images could be used for this goal. From the routinely measured otolith data of plaice (Pleuronectes platessa) in the eastern English Channel collected from 2010 to 2019, all methodological steps were developed under R environment. A greyscale images matrix was generated from the raw images in different formats. Contour detection was 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, 8 683 standardized right whole otoliths were extracted from 11 274 routine images. This method could be used on multiple species for age determination and stock identification.