TY  - CONF
AU  - Lluis Gomez
AU  - Andres Mafla
AU  - Marçal Rusiñol
AU  - Dimosthenis Karatzas
A2  - ECCV
PY  - 2018//
TI  - Single Shot Scene Text Retrieval
T2  - LNCS
BT  - 15th European Conference on Computer Vision
SP  - 728
EP  - 744
VL  - 11218
KW  - Image retrieval
KW  - Scene text
KW  - Word spotting
KW  - Convolutional Neural Networks
KW  - Region Proposals Networks
KW  - PHOC
N2  - Textual information found in scene images provides high level semantic information about the image and its context and it can be leveraged for better scene understanding. In this paper we address the problem of scene text retrieval: given a text query, the system must return all images containing the queried text. The novelty of the proposed model consists in the usage of a single shot CNN architecture that predicts at the same time bounding boxes and a compact text representation of the words in them. In this way, the text based image retrieval task can be casted as a simple nearest neighbor search of the query text representation over the outputs of the CNN over the entire imagedatabase. Our experiments demonstrate that the proposed architectureoutperforms previous state-of-the-art while it offers a significant increasein processing speed.
UR  - https://doi.org/10.1007/978-3-030-01264-9_43
L1  - http://refbase.cvc.uab.es/files/GMR2018.pdf
N1  - DAG; 600.084; 601.338; 600.121; 600.129
ID  - Lluis Gomez2018
ER  -