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Text Line Segmentation: A FCN Based Approach

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Computer Vision and Image Processing (CVIP 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1377))

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Abstract

Text line segmentation is a prerequisite for most of the document processing systems. However, for handwritten/warped documents, it is not straightforward to segment the text lines. This work proposes a learning-based text line segmentation method from document images. This work can tackle complex layouts present in a camera captured or handwritten document images along with printed flat-bed scanned English documents. The method also works for Alphasyllabrary scripts like Bangla. Segmentation of Bangla handwritten text is quite challenging because of its unique characteristics. The proposed approach of line segmentation relies on fully convolutional networks (FCNs). To improve the performance of the method, we introduce a post-processing step. The model is trained and tested on our dataset along with the cBAD dataset. We develop the model in such a way that it can be trained and tested in a machine that has limited access to highly computational accessories like GPU. The results of our experiments are encouraging.

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Authors and Affiliations

  1. Indian Institute of Engineering Sciences and Technology, Shibpur, Howrah, India

    Annie Minj, Arpan Garai & Sekhar Mandal

Authors
  1. Annie Minj
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  2. Arpan Garai
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  3. Sekhar Mandal
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Corresponding author

Correspondence to Arpan Garai .

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Editors and Affiliations

  1. Indian Institute of Information Technology Allahabad, Prayagraj, India

    Satish Kumar Singh

  2. Indian Institute of Technology Roorkee, Roorkee, India

    Partha Roy

  3. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  4. Indian Institute of Information Technology Allahabad, Prayagraj, India

    P. Nagabhushan

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Minj, A., Garai, A., Mandal, S. (2021). Text Line Segmentation: A FCN Based Approach. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1377. Springer, Singapore. https://doi.org/10.1007/978-981-16-1092-9_26

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  • DOI: https://doi.org/10.1007/978-981-16-1092-9_26

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-1091-2

  • Online ISBN: 978-981-16-1092-9

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