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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes

verfasst von : Pengyuan Lyu, Minghui Liao, Cong Yao, Wenhao Wu, Xiang Bai

Erschienen in: Computer Vision – ECCV 2018

Verlag: Springer International Publishing

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Abstract

Recently, models based on deep neural networks have dominated the fields of scene text detection and recognition. In this paper, we investigate the problem of scene text spotting, which aims at simultaneous text detection and recognition in natural images. An end-to-end trainable neural network model for scene text spotting is proposed. The proposed model, named as Mask TextSpotter, is inspired by the newly published work Mask R-CNN. Different from previous methods that also accomplish text spotting with end-to-end trainable deep neural networks, Mask TextSpotter takes advantage of simple and smooth end-to-end learning procedure, in which precise text detection and recognition are acquired via semantic segmentation. Moreover, it is superior to previous methods in handling text instances of irregular shapes, for example, curved text. Experiments on ICDAR2013, ICDAR2015 and Total-Text demonstrate that the proposed method achieves state-of-the-art results in both scene text detection and end-to-end text recognition tasks.

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Metadaten
Titel
Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes
verfasst von
Pengyuan Lyu
Minghui Liao
Cong Yao
Wenhao Wu
Xiang Bai
Copyright-Jahr
2018
Buch
Computer Vision – ECCV 2018

Print ISBN: 978-3-030-01263-2

Electronic ISBN: 978-3-030-01264-9

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-01264-9_5