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2015 | OriginalPaper | Chapter

A Machine Learning Approach to Hypothesis Decoding in Scene Text Recognition

Authors : Jindřich Libovický, Lukáš Neumann, Pavel Pecina, Jiří Matas

Published in: Computer Vision - ACCV 2014 Workshops

Publisher: Springer International Publishing

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Abstract

Scene Text Recognition (STR) is a task of localizing and transcribing textual information captured in real-word images. With its increasing accuracy, it becomes a new source of textual data for standard Natural Language Processing tasks and poses new problems because of the specific nature of Scene Text. In this paper, we learn a string hypotheses decoding procedure in an STR pipeline using structured prediction methods that proved to be useful in automatic Speech Recognition and Machine Translation. The model allow to employ a wide range of typographical and language features into the decoding process. The proposed method is evaluated on a standard dataset and improves both character and word recognition performance over the baseline.

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previous chapter Scene Text Recognition: No Country for Old Men?
next chapter Perspective Scene Text Recognition with Feature Compression and Ranking
Footnotes
1
We used the current version of TextSpotter available at http://​www.​textspotter.​org.
 
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Metadata
Title
A Machine Learning Approach to Hypothesis Decoding in Scene Text Recognition
Authors
Jindřich Libovický
Lukáš Neumann
Pavel Pecina
Jiří Matas
Copyright Year
2015
Book
Computer Vision - ACCV 2014 Workshops

Print ISBN: 978-3-319-16630-8

Electronic ISBN: 978-3-319-16631-5

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-16631-5_13

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