  The identification and characterization of various rock types is one of the
fundamental activities for geology and related areas such as mining, petroleum,
environment, industry and construction. Traditionally, a human specialist is
responsible for analyzing and explaining details about the type, composition,
texture, shape and other properties using rock samples collected in-situ or
prepared in a laboratory. The results become subjective based on experience, in
addition to consuming a large investment of time and effort. The present
proposal uses artificial intelligence techniques combining computer vision and
natural language processing to generate a textual and verbal description from a
thin section image of rock. We build a dataset of images and their respective
textual descriptions for the training of a model that associates the relevant
features of the image extracted by EfficientNetB7 with the textual description
generated by a Transformer network, reaching an accuracy value of 0.892 and a
BLEU value of 0.71. This model can be a useful resource for research,
professional and academic work, so it has been deployed through a Web
application for public use.
