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Neural Processing Letters
Erschienen in: Neural Processing Letters 2/2020

02.01.2020

Generating Text Sequence Images for Recognition

verfasst von: Yanxiang Gong, Linjie Deng, Zheng Ma, Mei Xie

Erschienen in: Neural Processing Letters | Ausgabe 2/2020

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Abstract

Recently, methods based on deep learning have dominated the field of text recognition. With a large number of training data, most of them can achieve the state-of-the-art performances. However, it is hard to harvest and label sufficient text sequence images from the real scenes. To mitigate this issue, several methods to synthesize text sequence images were proposed, yet they usually need complicated preceding or follow-up steps. In this work, we present a method which is able to generate infinite training data without any auxiliary pre/post-process. We tackle the generation task as an image-to-image translation one and utilize conditional adversarial networks to produce realistic text sequence images in the light of the semantic ones. Some evaluation metrics are involved to assess our method and the results demonstrate that the caliber of the data is satisfactory. The code and dataset will be publicly available soon.
Vorheriger Artikel Exponential Lag Synchronization and Global Dissipativity for Delayed Fuzzy Cohen–Grossberg Neural Networks with Discontinuous Activations
Nächster Artikel Growing Self-Organizing Maps for Nonlinear Time-Varying Function Approximation

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Fußnoten
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Metadaten
Titel
Generating Text Sequence Images for Recognition
verfasst von
Yanxiang Gong
Linjie Deng
Zheng Ma
Mei Xie
Publikationsdatum
02.01.2020
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2020
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-019-10166-x

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