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International Conference on Image Analysis and Processing

ICIAP 2022: Image Analysis and Processing – ICIAP 2022 pp 249–262Cite as

Skew Angle Detection and Correction in Text Images Using RGB Gradient

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13232))

Abstract

Detecting and correcting skew angles is critical to success in document layout analysis and optical character recognition tasks, as they are more susceptible to failure when on uneven skews. In automation such as postal systems, library management, office business, and banking data entry, skew angle estimation is crucial to improve procedures response. Although different works have addressed this subject, due to the variability in the input data, many solutions are restricted to a specific language, texts whose contents are within a controlled scope, and entries that differentiate printed from handwritten texts. This paper introduces a new method based on RGB gradient capable of detecting and correcting skew angles in different types of documents. We evaluate the proposed method using two public databases and compare our results with other techniques cited in the literature. In general, our proposal achieved results superior to the approaches compared in all groups of documents in the database. Furthermore, we show that our method can work accurately in various text orientations, and it can work efficiently against documents containing short and sparse text lines, non-textual objects, and image noises caused by imperfect scanning.

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Notes

  1. 1.

    https://gmic.eu/oldtutorial/_gradient2rgb.shtml.

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Acknowledgments

Authors thank to Coordination for the Improvement of Higher Education Personnel (CAPES Finance Code #001) and Instituto Federal Goiano, câmpus Urutaí (Process Number: 23219.000404.2022-67), for their financial support.

Author information

Authors and Affiliations

  1. Institute of Informatics, Universidade Federal de Goiás, Goiânia, GO, Brazil

    Bruno Rocha, Gabriel Vieira, Afonso Fonseca, Deborah Fernandes & Fabrizzio Soares

  2. Computer Vision Laboratory, Federal Institute Goiano, Urutaí, GO, Brazil

    Gabriel Vieira, Júnio César de Lima & Júlio César Ferreira

  3. Institute of Computing, University of Campinas, Campinas, SP, Brazil

    Helio Pedrini

Authors
  1. Bruno Rocha
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  2. Gabriel Vieira
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  3. Helio Pedrini
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  4. Afonso Fonseca
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  5. Deborah Fernandes
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  6. Júnio César de Lima
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  8. Fabrizzio Soares
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Corresponding author

Correspondence to Fabrizzio Soares .

Editor information

Editors and Affiliations

  1. Boston University, Boston, MA, USA

    Stan Sclaroff

  2. National Research Council, Lecce, Italy

    Cosimo Distante

  3. National Research Council, Lecce, Italy

    Marco Leo

  4. University of Catania, Catania, Italy

    Giovanni M. Farinella

  5. Technische Universität München, Garching, Germany

    Federico Tombari

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Rocha, B. et al. (2022). Skew Angle Detection and Correction in Text Images Using RGB Gradient. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13232. Springer, Cham. https://doi.org/10.1007/978-3-031-06430-2_21

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  • DOI: https://doi.org/10.1007/978-3-031-06430-2_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06429-6

  • Online ISBN: 978-3-031-06430-2

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