research-article

Space Displacement Localization Neural Networks to locate origin points of handwritten text lines in historical documents

Authors:

Bastien Moysset,

Christian Wolf,

Jérôme LouradourAuthors Info & Claims

HIP '15: Proceedings of the 3rd International Workshop on Historical Document Imaging and Processing

Pages 1 - 8

https://doi.org/10.1145/2809544.2809546

Published: 22 August 2015 Publication History

Abstract

We describe a new method for detecting and localizing multiple objects in an image using context aware deep neural networks. Common architectures either proceed locally per pixel-wise sliding-windows, or globally by predicting object localizations for a full image. We improve on this by training a semi-local model to detect and localize objects inside a large image region, which covers an object or a part of it. Context knowledge is integrated, combining multiple predictions for different regions through a spatial context layer modeled as an LSTM network.

The proposed method is applied to a complex problem in historical document image analysis, where we show that is capable of robustly detecting text lines in the images from the ANDAR-TL competition. Experiments indicate that the model can cope with difficult situations and reach the state of the art in Vision such as other deep models.

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Cited By

Suleymanov UHuseynov VManafova IMammadli AJafarov T(2023)Instance Segmentation of Handwritten Text on Historical Document Images Using Deep Learning Approaches4th International Conference on Artificial Intelligence and Applied Mathematics in Engineering10.1007/978-3-031-31956-3_20(244-253)Online publication date: 27-May-2023
https://doi.org/10.1007/978-3-031-31956-3_20
Dutta AGarai ABiswas SDas A(2021)Segmentation of text lines using multi-scale CNN from warped printed and handwritten document imagesInternational Journal on Document Analysis and Recognition (IJDAR)10.1007/s10032-021-00370-8Online publication date: 21-May-2021
https://doi.org/10.1007/s10032-021-00370-8
Gao ZLiu JLi YYang YHe H(2020)A Novel Semantic Segmentation Model for Chinese CharactersIEEE Access10.1109/ACCESS.2020.30270198(179083-179093)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3027019
Show More Cited By

Space Displacement Localization Neural Networks to locate origin points of handwritten text lines in historical documents
1. Computing methodologies
  1. Artificial intelligence
  2. Machine learning
    1. Machine learning approaches

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cover image ACM Other conferences

HIP '15: Proceedings of the 3rd International Workshop on Historical Document Imaging and Processing

August 2015

155 pages

ISBN:9781450336024

DOI:10.1145/2809544

Editors:
Bertrand Coüasnon
Irisa / Insa de Rennes, France
,
Volker Märgner
Technische Universität Braunschweig, Germany
,
Volkmar Frinken
Electrical and Computer Engineering, UC Davis, USA and ONU Technology, San Jose, USA
,
Bill Barrett
Brigham Young University, USA
,
Program Chairs:
Bertrand Coüasnon
Irisa / Insa de Rennes, France
,
Volkmar Frinken
Electrical and Computer Engineering, UC Davis, USA and ONU Technology, San Jose, USA
,
Hamid Amiri
ENIT, Tunisia
,
Volker Märgner
Technische Universität Braunschweig, Germany
,
Bill Barrett
Brigham Young University, USA

Copyright © 2015 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 the author(s) 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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New York, NY, United States

Publication History

Published: 22 August 2015

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HIP '15

HIP '15: 3rd International Workshop on Historical Document Imaging and Processing

August 22, 2015

Gammarth, Tunisia

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Overall Acceptance Rate 52 of 90 submissions, 58%

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Cited By

Suleymanov UHuseynov VManafova IMammadli AJafarov T(2023)Instance Segmentation of Handwritten Text on Historical Document Images Using Deep Learning Approaches4th International Conference on Artificial Intelligence and Applied Mathematics in Engineering10.1007/978-3-031-31956-3_20(244-253)Online publication date: 27-May-2023
https://doi.org/10.1007/978-3-031-31956-3_20
Dutta AGarai ABiswas SDas A(2021)Segmentation of text lines using multi-scale CNN from warped printed and handwritten document imagesInternational Journal on Document Analysis and Recognition (IJDAR)10.1007/s10032-021-00370-8Online publication date: 21-May-2021
https://doi.org/10.1007/s10032-021-00370-8
Gao ZLiu JLi YYang YHe H(2020)A Novel Semantic Segmentation Model for Chinese CharactersIEEE Access10.1109/ACCESS.2020.30270198(179083-179093)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3027019
Tensmeyer CMartinez T(2019)Robust Keypoint Detection2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)10.1109/ICDARW.2019.40072(1-7)Online publication date: Sep-2019
https://doi.org/10.1109/ICDARW.2019.40072
Abdi LMeddeb A(2018)Driver information systemMultimedia Tools and Applications10.1007/s11042-017-5054-677:12(14673-14703)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1007/s11042-017-5054-6
Renton GSoullard YChatelain CAdam SKermorvant CPaquet T(2018)Fully convolutional network with dilated convolutions for handwritten text line segmentationInternational Journal on Document Analysis and Recognition10.1007/s10032-018-0304-321:3(177-186)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s10032-018-0304-3
Renton GChatelain CAdam SKermorvant CPaquet T(2017)Handwritten Text Line Segmentation Using Fully Convolutional Network2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)10.1109/ICDAR.2017.321(5-9)Online publication date: Nov-2017
https://doi.org/10.1109/ICDAR.2017.321
Bluche T(2016)Joint line segmentation and transcription for end-to-end handwritten paragraph recognitionProceedings of the 30th International Conference on Neural Information Processing Systems10.5555/3157096.3157190(838-846)Online publication date: 5-Dec-2016
https://dl.acm.org/doi/10.5555/3157096.3157190
Moysset BLouradour JKermorvant CWolf C(2016)Learning Text-Line Localization with Shared and Local Regression Neural Networks2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)10.1109/ICFHR.2016.0014(1-6)Online publication date: Oct-2016
https://doi.org/10.1109/ICFHR.2016.0014

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