Elsevier

Pattern Recognition Letters

Volume 15, Issue 12, December 1994, Pages 1201-1209
Pattern Recognition Letters

Segmentation and classification of mixed text/graphics/image documents

https://doi.org/10.1016/0167-8655(94)90110-4Get rights and content

Abstract

In this paper, a feature-based document analysis system is presented which utilizes domain knowledge to segment and classify mixed text/graphics/image documents. In our approach, we first perform a run-length smearing operation followed by the stripe merging procedure to segment the blocks embedded in a document. The classification task is then performed based on the domain knowledge induced from the primitives associated with each type of medium. Proper use of domain knowledge is proved to be effective in accelerating the segmentation speed and decreasing the classification error. The experimental study reveals the feasibility of the new technique in segmenting and classifying mixed text/graphics/image documents.

References (7)

  • T. Pavlidis et al.

    Page segmentation and classification

    CVGIP: Graphical Models and Image Processing

    (1992)
  • G. Nagy et al.

    Hierarchical representation of optically scanned documents

  • A. Rosenfeld et al.
There are more references available in the full text version of this article.

Cited by (50)

  • Complex layout analysis based on contour classification and morphological operations

    2017, Engineering Applications of Artificial Intelligence
    Citation Excerpt :

    Tsujimoto and Asada use the same smearing algorithm in order to aggregate adjacent connected components into segments by connecting two black runs separated by a small gap (Tsujimoto and Asada, 1992). Fan et al. also perform a run-length smearing operation and then merge consecutive horizontal stripes into paragraphs (Fan et al., 1994). Sun proposed a modified smearing algorithm, called selective CRLA, capable of processing documents with non-Manhattan (containing non-rectangular blocks) layouts (Sun, 2005).

  • Tensor representation learning based image patch analysis for text identification and recognition

    2015, Pattern Recognition
    Citation Excerpt :

    Subsequently, text lines are combined into blocks. Based on the run-length smearing algorithm (RLSA) [19], Fan et al. [20] proposed a document analysis system. RLSA has the effect of linking together neighboring black areas that are separated by less than L pixels, where L is a predefined number.

  • Background grid extraction from historical hand-drawn cadastral maps

    2023, International Journal on Document Analysis and Recognition
  • Can Deep Learning Approaches Detect Complex Text? Case of Onomatopoeia in Comics Albums

    2023, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
  • Document Region Segmentation

    2023, SpringerBriefs in Computer Science
View all citing articles on Scopus

This work is supported by National Science Council of Taiwan under grant NSC-83-0408-E-008-001.

View full text