Computers that learn to read signs without any real photos
Imagine a program that learns to read street signs, store fronts, and posters without ever seeing a real photo labelled by a person.
It learns from pictures made by a computer, pictures that look real enough to teach it.
This lets researchers have infinite data to train on, so mistakes from little datasets go away and learning gets faster, even weird fonts and backgrounds are covered.
The heart of the trick is a set of neural networks that look at a whole word at once, not letter by letter.
The networks are trained only on those fake-but-real looking images, so the system don't need humans to tag anything — that means zero data-acquisition costs.
The teams tried a few ways of letting the machines read: one picks from a huge dictionary, another spells out each character, and one guesses patterns inside words.
All of them got much better at reading text in photos, even when nothing was constrained.
This shows synthetic data can replace real pictures, and machines can learn to read the messy world faster than before.
Read article comprehensive review in Paperium.net:
Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition
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