Deep structured output learning for unconstrained text recognition
We develop a representation suitable for the unconstrained recognition of words in natural images: the general case of no fixed lexicon and unknown length. To this end we propose a convolutional neural network (CNN) based architecture which incorporates a Conditional Random Field (CRF) graphical mod...
| Main Authors: | , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
International Conference on Learning Representations
2015
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