Binarization of Engineering Drawings Using Adaptive Thresholding Techniques

  • Shih-Chang Liang
  • Wen-Jan Chen
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 4)

Image binarization is an important issue in image segmentation. In recent years, numerous studies have been focused on this area. Navon et al. [5] described the segmentation methods including histogram-based methods, boundary-based methods, region-based methods, hybrid-based methods, and graph-based technique. When the threshold method is applied to image binarization, either a global threshold or local threshold method will be used. The former method uses histogram analysis to search for a single threshold in an image and differentiate objects and background according to a comparison of all the pixels within the threshold. The latter method first segments an image into individual blocks and calculates the threshold of each block or even each pixel.


Image Binarization Document Image Threshold Method Global Threshold Local Threshold 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Deng W, Iyengar SS, Brener NE (2000) A fast parallel thinning algorithm for the binary image skeletonization. The International Journal of High Performance Computing Applications 14:65–81CrossRefGoogle Scholar
  2. 2.
    Gatos B, Pratikakis I, Perantonis SJ (2004) An adaptive binarization technique for low quality historical documents. Marinai S, Dengel A (eds.) D AS, LNCS 3163, 2004, pp. 102–113Google Scholar
  3. 3.
    Huang Q, Gao W, Cai W (2005) Thresholding technique with adaptive window selection for uneven lighting image. Pattern Recognition Letters 26:801–808CrossRefGoogle Scholar
  4. 4.
    Lee K-H, Cho S-B, Choy Y-C (2000) Automated vectorization of cartographic maps by a knowledge-based system. Engineering Applications of Artificial Intelligence 13:165–178CrossRefGoogle Scholar
  5. 5.
    Navon E, Miller O, Averbuch A (2005) Color image segmentation based on adaptive local thresholds. Image and Vision Computing 23:69–85CrossRefGoogle Scholar
  6. 6.
    O’Gorman L (1994) Binarization and multithresholding of document images using connectivity. CVGIP: Graphical Models and Image Processing 56:494–506CrossRefGoogle Scholar
  7. 7.
    Sauvola., Pietikainen M (2000) Adaptive document image binarization. Pattern Recognition 33:225–236CrossRefGoogle Scholar
  8. 8.
    Smith EHB (2002) Uniqueness of bilevel image degradations. SPIE Document Recognition and Retrieval VIII, San Jose, CA, pp. 20–25Google Scholar
  9. 9.
    Yang Y, Yan H (2000) An adaptive logical method for binarization of degraded document images. Pattern Recognition 33:787–807CrossRefGoogle Scholar
  10. 10.
    Zhao M, Yang Y, and Yan H (2000) An adaptive thresholding method for binarization of blueprint images. Pattern Recognition Letters.21:927–943CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Shih-Chang Liang
    • 1
  • Wen-Jan Chen
    • 1
  1. 1.Department of Computer Science and Information EngineeringDaYeh UniversityChina

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