This is most common and popular use case of deep neural networks, where the algorithms are trained on large image datasets to classify them to reach sufficient accuracy (achieving almost human image recognition and later surpassing humans).
For instance, in mammography, deep learning meets medical imaging in the interpretation of radiologic images to successfully detect or characterize abnormalities on digital images.
Radiologists supplied with this information often perform better at mammographic detection or characterization tasks in observer studies than do unaided radiologists.
Deep learning therefore could decrease errors in mammographic interpretation that continue to plague human observers.
Handwriting analysis for banks are definitely in the works, and not forgetting facial recognition for security purposes at both Apple and MasterCard, where users are able to unlock future iPhones and verify credit card payments by taking a selfie.
By feeding the neural networks with large amounts of data, the machines are able to identify, tag and describe the objects found in images, regardless of the database size.
This goes towards saving man hours during product tagging and attribution extraction processes in the e-comm...vity for these players.
We are already seeing self-driving cars, where deep learning software integrates with automated driving systems to detect and interpret real-life situations on the road.