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Improving Text Proposals for Scene Images with Fully Convolutional Networks

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arxiv 1702.05089 v1 pith:ADEGW2TX submitted 2017-02-16 cs.CV

Dena Bazazian , Raul Gomez , Anguelos Nicolaou , Lluis Gomez , Dimosthenis Karatzas , Andrew D. Bagdanov This is my paper · ORCID
classification cs.CV
keywords proposalstextconvolutionalfullynetworksobjectresultsscene
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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 Gomez and Karatzas (2016), 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.

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  1. ROSA: Addressing text understanding challenges in photographs via ROtated SAmpling

    cs.CL 2025-06 conditional novelty 5.0 of 10

    ROSA, which combines four image rotations with likelihood-ranked sampling, improves VQA accuracy on misoriented text by up to 11.7 absolute points over greedy decoding.

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