research-article

A framework for the assessment of text extraction algorithms on complex colour images

Authors:

A. Clavelli,

D. Karatzas,

J. LladósAuthors Info & Claims

DAS '10: Proceedings of the 9th IAPR International Workshop on Document Analysis Systems

Pages 19 - 26

https://doi.org/10.1145/1815330.1815333

Published: 09 June 2010 Publication History

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Abstract

The availability of open, ground-truthed datasets and clear performance metrics is a crucial factor in the development of an application domain. The domain of colour text image analysis (real scenes, Web and spam images, scanned colour documents) has traditionally suffered from a lack of a comprehensive performance evaluation framework. Such a framework is extremely difficult to specify, and corresponding pixel-level accurate information tedious to define. In this paper we discuss the challenges and technical issues associated with developing such a framework. Then, we describe a complete framework for the evaluation of text extraction methods at multiple levels, provide a detailed ground-truth specification and present a case study on how this framework can be used in a real-life situation.

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Sarkhel RNandi A(2021)Improving information extraction from visually rich documents using visual span representationsProceedings of the VLDB Endowment10.14778/3446095.344610414:5(822-834)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.14778/3446095.3446104
Xu XZhang ZWang ZPrice BWang ZShi H(2021)Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement Approach2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR46437.2021.01187(12040-12050)Online publication date: Jun-2021
https://doi.org/10.1109/CVPR46437.2021.01187
Makhmudov FMukhiddinov MAbdusalomov AAvazov KKhamdamov UCho Y(2020)Improvement of the end-to-end scene text recognition method for “text-to-speech” conversionInternational Journal of Wavelets, Multiresolution and Information Processing10.1142/S021969132050052618:06(2050052)Online publication date: 15-Sep-2020
https://doi.org/10.1142/S0219691320500526
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Index Terms

A framework for the assessment of text extraction algorithms on complex colour images
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results

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Published In

DAS '10: Proceedings of the 9th IAPR International Workshop on Document Analysis Systems

June 2010

490 pages

ISBN:9781605587738

DOI:10.1145/1815330

General Chairs:
David Doermann
University of Maryland, College Park
,
Venu Govindaraju
University at Buffalo, SUNY
,
Daniel Lopresti
Lehigh University
,
Prem Natarajan
Raytheon BBN Technologies

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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Association for Computing Machinery

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Published: 09 June 2010

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DAS '10

DAS '10: The Eighth IAPR International Workshop on Document Analysis Systems

June 9 - 11, 2010

Massachusetts, Boston, USA

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Sarkhel RNandi A(2021)Improving information extraction from visually rich documents using visual span representationsProceedings of the VLDB Endowment10.14778/3446095.344610414:5(822-834)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.14778/3446095.3446104
Xu XZhang ZWang ZPrice BWang ZShi H(2021)Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement Approach2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR46437.2021.01187(12040-12050)Online publication date: Jun-2021
https://doi.org/10.1109/CVPR46437.2021.01187
Makhmudov FMukhiddinov MAbdusalomov AAvazov KKhamdamov UCho Y(2020)Improvement of the end-to-end scene text recognition method for “text-to-speech” conversionInternational Journal of Wavelets, Multiresolution and Information Processing10.1142/S021969132050052618:06(2050052)Online publication date: 15-Sep-2020
https://doi.org/10.1142/S0219691320500526
Ghoshal RBanerjee A(2020)Region Growing-Based Scheme for Extraction of Text from Scene ImagesProceedings of International Conference on Frontiers in Computing and Systems10.1007/978-981-15-7834-2_14(149-155)Online publication date: 24-Nov-2020
https://doi.org/10.1007/978-981-15-7834-2_14
Huang X(2019)A new video text extraction using local laplacian filters and mean shiftMultimedia Tools and Applications10.1007/s11042-018-6451-178:6(6989-7004)Online publication date: 1-Mar-2019
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Ghoshal RBanerjee A(2019)SVM and MLP Based Segmentation and Recognition of Text from Scene Images Through an Effective Binarization SchemeComputational Intelligence in Pattern Recognition10.1007/978-981-13-9042-5_20(237-246)Online publication date: 18-Aug-2019
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Ghoshal RRoy ABanerjee ADhara BParui S(2018)A novel method for binarization of scene text images and its application in text identificationPattern Analysis and Applications10.1007/s10044-018-0687-2Online publication date: 14-Feb-2018
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ari M(2017)Scene text segmentation using low variation extremal regions and sorting based character groupingNeurocomputing10.1016/j.neucom.2017.05.021266:C(56-65)Online publication date: 29-Nov-2017
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Mishra AAlahari KJawahar C(2017)Unsupervised refinement of color and stroke features for text binarizationInternational Journal on Document Analysis and Recognition10.1007/s10032-017-0283-920:2(105-121)Online publication date: 1-Jun-2017
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Calarasanu SFabrizio JDubuisson S(2016)From Text Detection to Text Segmentation: A Unified Evaluation SchemeComputer Vision – ECCV 2016 Workshops10.1007/978-3-319-46604-0_28(378-394)Online publication date: 18-Sep-2016
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Color-based clustering for text detection and extraction in image

Color-Based Text Extraction for the Image

An efficient image retrieval scheme for colour enhancement of embedded and distributed surveillance images

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