2009
DOI: 10.1587/transinf.e92.d.689
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Enhancing Salt-and-Pepper Noise Removal in Binary Images of Engineering Drawing

Abstract: SUMMARYNoise removal in engineering drawing is an important operation performed before other image analysis tasks. Many algorithms have been developed to remove salt-and-pepper noise from document images. Cleaning algorithms should remove noise while keeping the real part of the image unchanged. Some algorithms have disadvantages in cleaning operation that leads to removing of weak features such as short thin lines. Others leave the image with hairy noise attached to image objects. In this article a noise remo… Show more

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Cited by 5 publications
(2 citation statements)
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“…It is concluded from the experimental results that the Eigenfaces method is suitable for font recognition of degraded documents. The three-percentage incorrect classification can be corrected by utilizing noise removal algorithms (25,26,27) and/or relying on intra-word font information. In addition, the good overall accuracy of the Eigenfaces module suggests that adding/porting it to Decapod system is feasible and will enable the creation of type 5 PDF files, i.e.…”
Section: Resultsmentioning
confidence: 99%
“…It is concluded from the experimental results that the Eigenfaces method is suitable for font recognition of degraded documents. The three-percentage incorrect classification can be corrected by utilizing noise removal algorithms (25,26,27) and/or relying on intra-word font information. In addition, the good overall accuracy of the Eigenfaces module suggests that adding/porting it to Decapod system is feasible and will enable the creation of type 5 PDF files, i.e.…”
Section: Resultsmentioning
confidence: 99%
“…The three factors were noise-removal method, noise level, and raster-to-vector method. The study was performed using six noise-removal methods: kFill [17], [18], Enhanced kFill (EkFill) [19], Activity Detector (AD) [20], and their respective enhanced counterparts Algorithm A (AlgA) [21], Algorithm B (AlgB) [22], and Algorithm C (AlgC) [23]. Three noise levels were studied: 5%, 10% and 15%.…”
Section: Copyright C 2011 the Institute Of Electronics Information Amentioning
confidence: 99%