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Author Dena Bazazian; Raul Gomez; Anguelos Nicolaou; Lluis Gomez; Dimosthenis Karatzas; Andrew Bagdanov
Title Improving Text Proposals for Scene Images with Fully Convolutional Networks Type Conference Article
Year 2016 Publication 23rd International Conference on Pattern Recognition Workshops Abbreviated Journal
Volume Issue Pages
Abstract Text Proposals have emerged as a class-dependent version of object proposals – efficient approaches to reduce the search space of possible text object locations in an image. Combined with strong word classifiers, text proposals currently yield top state of the art results in end-to-end scene text
recognition. In this paper we propose an improvement over the original Text Proposals algorithm of [1], combining it with Fully Convolutional Networks to improve the ranking of proposals. Results on the ICDAR RRC and the COCO-text datasets show superior performance over current state-of-the-art.
Address (up) Cancun; Mexico; December 2016
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
Area Expedition Conference ICPRW
Notes DAG; LAMP; 600.084 Approved no
Call Number Admin @ si @ BGN2016 Serial 2823
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