research-article

Enhancement Spatial Transformer Networks for Text Classification

Authors:

Wei KeAuthors Info & Claims

ICGSP '20: Proceedings of the 4th International Conference on Graphics and Signal Processing

Pages 5 - 10

https://doi.org/10.1145/3406971.3406981

Published: 29 July 2020 Publication History

Abstract

This paper introduces a 2D transformation based framework for arbitrary-oriented text detection in natural scene images. We present the localization networks within Spatial Transformer Networks (STN), which are designed to generate proposals with text orientation affine information including translation, scaling and rotation. This information will then be adapted as learning parameters to make the proposals to be fitted into the text regular form in terms of the orientation more accurately. Localization network is proposed to project arbitrary-oriented proposals to a feature map for a text region classifier. Compared with any previous text detection systems, this work ensures the relationship between the learning parameters, which can lead to a better approximation for orientation. As a result, this new layer greatly enhances the training accuracy. Moreover, the design and implementation can be easily deployed in the current systems built upon the standard CNNs architecture.

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Cited By

Di Maio CNunziati GMecocci A(2024)Deep-Learning-Based Action and Trajectory Analysis for Museum Security VideosElectronics10.3390/electronics1307119413:7(1194)Online publication date: 25-Mar-2024
https://doi.org/10.3390/electronics13071194
Im SChan K(2023)Distributed Spatial Transformer for Object Tracking in Multi-Camera2023 25th International Conference on Advanced Communication Technology (ICACT)10.23919/ICACT56868.2023.10079540(122-125)Online publication date: 19-Feb-2023
https://doi.org/10.23919/ICACT56868.2023.10079540
Im SChan K(2023)A Study of Small Corpus-based NMT for Image-based Text Recognition2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS)10.1109/ICACCS57279.2023.10112894(1497-1501)Online publication date: 17-Mar-2023
https://doi.org/10.1109/ICACCS57279.2023.10112894
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Index Terms

Enhancement Spatial Transformer Networks for Text Classification
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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ICGSP '20: Proceedings of the 4th International Conference on Graphics and Signal Processing

June 2020

127 pages

ISBN:9781450377812

DOI:10.1145/3406971

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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University of Macedonia
NITech: Nagoya Institute of Technology
Zhejiang University: Zhejiang University

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Association for Computing Machinery

New York, NY, United States

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Published: 29 July 2020

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ICGSP 2020: 2020 The 4th International Conference on Graphics and Signal Processing

June 26 - 29, 2020

Nagoya, Japan

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Cited By

Di Maio CNunziati GMecocci A(2024)Deep-Learning-Based Action and Trajectory Analysis for Museum Security VideosElectronics10.3390/electronics1307119413:7(1194)Online publication date: 25-Mar-2024
https://doi.org/10.3390/electronics13071194
Im SChan K(2023)Distributed Spatial Transformer for Object Tracking in Multi-Camera2023 25th International Conference on Advanced Communication Technology (ICACT)10.23919/ICACT56868.2023.10079540(122-125)Online publication date: 19-Feb-2023
https://doi.org/10.23919/ICACT56868.2023.10079540
Im SChan K(2023)A Study of Small Corpus-based NMT for Image-based Text Recognition2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS)10.1109/ICACCS57279.2023.10112894(1497-1501)Online publication date: 17-Mar-2023
https://doi.org/10.1109/ICACCS57279.2023.10112894
Chen YKe WChan KXiong Z(2021)A Human Activity Recognition Approach Based on Skeleton Extraction and Image ReconstructionProceedings of the 5th International Conference on Graphics and Signal Processing10.1145/3474906.3474909(1-8)Online publication date: 25-Jun-2021
https://dl.acm.org/doi/10.1145/3474906.3474909

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