1. A computer-implemented method comprising: identifying, by one or more computing devices and from a plurality of images, one or more images that depict an entity; determining, by the one or more computing devices, location information associated with the one or more images that depict the entity; identifying, by the one or more computing devices and based at least in part on the location information associated with the one or more images that depict the entity, one or more candidate entity profiles from an entity directory; providing, by the one or more computing devices, the one or more images that depict the entity and the one or more candidate entity profiles as input to a machine learning model comprising a neural network and at least one recurrent neural network, the neural network comprising a deep convolutional neural network (CNN), the at least one recurrent neural network comprising a long short-term memory network (LSTM), the CNN being configured to receive data indicative of the one or more images that depict the entity, extract features from the one or more images that depict the entity, and provide data indicative of the extracted features to the LSTM, the LSTM being configured to receive data indicative of the one or more candidate entity profiles, obtain text-related information from the one or more candidate entity profiles, and model at least a portion of structured information from a candidate entity profile as a sequence of characters, such that a match score between the extracted features and data from the candidate entity profile can be determined; generating, by the one or more computing devices, one or more outputs of the machine learning model, each output comprising a match score associated with at least one candidate entity profile and an image that depicts the entity; and updating, by the one or more computing devices, the entity directory based at least in part on the one or more generated outputs of the machine learning model.