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Document segmentation using textural features summarization and feedforward neural network

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Abstract

Document Segmentation is a process that aims to filter documents while identifying certain regions of interest. Generally, the regions of interest include texts, graphics (image occupied regions) and the background. This paper presents a novel top-bottom approach to perform document segmentation using texture features that are extracted from the specified/selected documents. A mask of suitable size is used to summarize textural features, and statistical parameters are captured as blocks in document images. Four textural features that are extracted from masks using the gray level co-occurrence matrix (glcm) include entropy, contrast, energy and homogeneity. Furthermore, two statistical parameters extracted from corresponding masks are the modal and median pixel values. The extracted attributes allow the classification of each mask or block as text, graphics, and background. A feedforward network is trained on the 6 extracted attributes, using documents obtained from a public database ; an error rate of 15.77 % is achieved. Furthermore, it is shown that this novel approach produces promising performance in segmenting documents and is expected to be significantly efficient for content-based information retrieval systems. Detection of duplicate documents within large databases is another potential area of application.

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Acknowledgments

The authors wish to thank Assist. Prof. Dr. Pinar Akpinar, for proofreading this manuscript, and suggesting clearer re-expressions of ideas within the work.

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Authors and Affiliations

  1. Near East University, Lefkosa, via Mersin-10, North Cyprus

    Oyebade K. Oyedotun & Adnan Khashman

  2. Centre of Innovation for Artificial Intelligence, British University of Nicosia, Girne, via Mersin-10, North Cyprus

    Adnan Khashman

Authors
  1. Oyebade K. Oyedotun
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  2. Adnan Khashman
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Corresponding author

Correspondence to Oyebade K. Oyedotun.

Appendix

Appendix

Fig. 12
figure 12

Segmentation outcome for document 9

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Fig. 13
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Segmentation outcome for document 10

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Fig. 14
figure 14

Segmentation outcome for document 12

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Oyedotun, O.K., Khashman, A. Document segmentation using textural features summarization and feedforward neural network. Appl Intell 45, 198–212 (2016). https://doi.org/10.1007/s10489-015-0753-z

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