Learning Hypotheses Decoding in an Image Text Recognition Pipeline
Speaker:
Jindřich Libovický
Abstract:
In Image Text Recognition pipelines, usually some hypotheses
about the letters in the image are made first. In the next stage the
best hypotheses are selected based on some criteria of how letters in a
text can follow each other. The presented work is an extension of work
done at the Centre for Machine Perception of the Czech Technical
University. It is an application of the structured prediction methods on
learning decoding the hypotheses based on the image processing based on a rich set of both visual and language features.