An example of how advanced deep learning methods can be used to extract information from image data. The image data is already captured and stored. And its dynamic. Could also be used with other imagery, like from store shelf images. Note the common existence of noise in such recordings. Also the integrated normalization included in the tagging. Thinking other possibilities.
Enhancing Google Maps with Deep Learning and Street View by Srini Penchikala
" ... The deep learning model also automatically labels new Street View imagery, normalizes the text to be consistent with the naming conventions and ignores extraneous text that's not relevant for the data analytics. This allows the team to create new addresses directly from images without even knowing the name of the street or the location of the addresses. For example, when a Street View car drives on a newly built road, the model can analyze the captured images, extract the street names and numbers, and properly create and locate the new addresses automatically on Google Maps. .... "
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