16. One or more tangible, non-transitory computer-readable media storing computer-readable instructions that when executed by one or more processors cause the one or more processors to perform operations comprising: identifying, from a plurality of images, one or more images that depict an entity; determining location information associated with the one or more images that depict the entity; identifying, 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 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 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 the entity directory based at least in part on the one or more generated outputs of the machine learning model.
16. One or more tangible, non-transitory computer-readable media storing computer-readable instructions that when executed by one or more processors cause the one or more processors to perform operations comprising: identifying, from a plurality of images, one or more images that depict an entity; determining location information associated with the one or more images that depict the entity; identifying, 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 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 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 the entity directory based at least in part on the one or more generated outputs of the machine learning model. 19. The one or more tangible, non-transitory computer-readable media of claim 16 , wherein the entity is a business, and wherein the at least one candidate entity profile comprises data associated with the business.