Text recognition in a natural environment like cities, roads and businesses is a challenging computer vision (CV) and machine learning problem. This model is extensible to extract other types of information out of Street View images like the business names from store fronts. The new deep neural network model, now publicly available for use by developers, achieved a higher deep neural network (84.2%) in reading street names out of Street View images from the French Street Name Signs (FSNS) dataset. So one of the goals for the team is to automatically extract structured information from the geo-located images. Street View cars have collected 80 billion images to date and it's impossible to manually analyze this very large image data set to find new or updated information for Google Maps. Google Maps software is used for directions, real-time traffic information and information on businesses, however to provide a better experience to its over one billion users, the information has to reflect the changing world. Julian Ibarz ( Google Brain Team) and Sujoy Banerjee (Ground Truth Team) wrote on Google Research Blog website about this TensorFlow model used for solving real-world image text extraction problems. This neural network model achieved a higher accuracy in processing the challenging French Street Name Signs (FSNS) dataset. Google's Ground Truth team recently announced a new deep learning model for the automatic extraction of information from geo-located image files to improve Google Maps.
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