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Erschienen in:
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2015 | OriginalPaper | Buchkapitel

Accurate Scene Text Recognition Based on Recurrent Neural Network

verfasst von : Bolan Su, Shijian Lu

Erschienen in: Computer Vision – ACCV 2014

Verlag: Springer International Publishing

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Abstract

Scene text recognition is a useful but very challenging task due to uncontrolled condition of text in natural scenes. This paper presents a novel approach to recognize text in scene images. In the proposed technique, a word image is first converted into a sequential column vectors based on Histogram of Oriented Gradient (HOG). The Recurrent Neural Network (RNN) is then adapted to classify the sequential feature vectors into the corresponding word. Compared with most of the existing methods that follow a bottom-up approach to form words by grouping the recognized characters, our proposed method is able to recognize the whole word images without character-level segmentation and recognition. Experiments on a number of publicly available datasets show that the proposed method outperforms the state-of-the-art techniques significantly. In addition, the recognition results on publicly available datasets provide a good benchmark for the future research in this area.

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Vorheriges Kapitel Submodular Reranking with Multiple Feature Modalities for Image Retrieval
Nächstes Kapitel Massive City-Scale Surface Condition Analysis Using Ground and Aerial Imagery
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Metadaten
Titel
Accurate Scene Text Recognition Based on Recurrent Neural Network
verfasst von
Bolan Su
Shijian Lu
Copyright-Jahr
2015
Verlag
Springer International Publishing
Buch
Computer Vision – ACCV 2014

Print ISBN: 978-3-319-16864-7

Electronic ISBN: 978-3-319-16865-4

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-16865-4_3

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