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Foreground Text Extraction in Color Document Images for Enhanced Readability

  • S. Nirmala
  • P. Nagabhushan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5909)

Abstract

Quite often it is observed that text information in documents is printed on colorful complex background. Smooth reading of text content in such documents is difficult due to background patterns and mix up of foreground text color with background color. Further the character recognition rate when such documents are OCRed, is low. In this paper we are presenting a novel approach for extraction of text information in complex color document images. The proposed approach is a three stage process. In the first stage the edge map is obtained utilizing the Canny edge operator. The edge map is split into blocks of uniform size and image blocks are classified as text or non-text. In each text block the possible text regions are identified and enclosed in tight bounding boxes using x-y cut on edge pixels. Further the text regions that are immediate adjacent to each other in vertical direction in which the character(s) are split horizontally are merged so as to enclose the character(s) fully in one text region. In the second stage certain amount of false text regions are eliminated based on a property of printed text. In the last stage the foreground text in each text region is extracted by unsupervised thresholding using the data of refined text regions. We conducted exhaustive experiments on documents having variety of background complexities with printed foreground text in any color, font and tilt. The experimental evaluations show that on an average 98.03% of text is identified. The processed document images showed better performance when OCRed compared with the corresponding unprocessed source document images.

Keywords

Color document image Complex background Foreground Text extraction Text region detection Unsupervised thresholding OCR 

References

  1. 1.
    Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic imaging 13, 146–165 (2004)CrossRefGoogle Scholar
  2. 2.
    Pietikäinen, M., Okun, O.: Text extraction from grey scale page images by simple edge detectors. In: Proceedings of the 12th Scandinavian Conference on Image Analysis, SCIA, Norway, pp. 628–635 (2001)Google Scholar
  3. 3.
    Leedham, G., Chen, Y., Takru, K., Tan, J.H.N., Mian, L.: Comparison of some thresholding algorithms for text/background segmentation in difficult document images. In: Proceedings of seventh International Conf. on Document Analysis and Recognition (ICDAR), pp. 859–864 (2003)Google Scholar
  4. 4.
    Shivananda, N., Nagabhushan, P.: Separation of Foreground Text from Complex Background in Color Document Images. In: Proceedings of Seventh international conference on advances in pattern recognition, ISI Kolkata, pp. 306–309 (2009)Google Scholar
  5. 5.
    Kasar, T., Kumar, J., Ramakrishnan, A.G.: Font and Background Color Independent Text Binarization. In: Proceedings of 2nd Intl. workshop on Camera Based Document Analysis and Recognition (workshop of CBDAR), pp. 3–9 (2007)Google Scholar
  6. 6.
    Liu, Y., Goto, S., Ikenaga, T.: A contour based robust algorithm for text detection in color images. IEICE Transactions on Information and Systems 89, 1221–1230 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • S. Nirmala
    • 1
  • P. Nagabhushan
    • 1
  1. 1.Dept of Studies in Computer ScienceUniversity of MysoreMysoreIndia

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