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ASKME: adaptive sampling with knowledge-driven vectorization of mechanical engineering drawings

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Abstract

We propose here an efficient algorithm for high-level vectorization of scanned images of mechanical engineering drawings. The algorithm is marked by several novel features, which merit its superiority over the existing techniques. After preprocessing and necessary refinement of junction points in the image skeleton, it first extracts the graphic primitives, such as lines, circles, and arcs, based on certain digital geometric properties of straightness and circularity in the discrete domain. The primitives are classified into different types with all associated details based on fast and efficient geometric analysis. The vector set is succinctly reduced by such classification in tandem with further consolidation to make out meaningful objects like rectangles and annuli, together with hatching information. Exhaustive testing shows the efficiency of the algorithm and also its robustness and stability toward any affine transformation and injected noise. Easy reconstruction to scalable vector graphics demonstrates its readiness and usability as a state-of-the-art solution.

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Acknowledgments

We are thankful to the anonymous reviewers for their detailed reviews and constructive comments, which have helped us in presenting the paper up to its merit.

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Authors and Affiliations

  1. Indian Institute of Engineering Science and Technology, Shibpur, India

    Paramita De, Sekhar Mandal & Amit Das

  2. Indian Institute Technology, Kharagpur, India

    Partha Bhowmick

Authors
  1. Paramita De
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  2. Sekhar Mandal
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  3. Partha Bhowmick
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  4. Amit Das
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Correspondence to Partha Bhowmick.

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De, P., Mandal, S., Bhowmick, P. et al. ASKME: adaptive sampling with knowledge-driven vectorization of mechanical engineering drawings. IJDAR 19, 11–29 (2016). https://doi.org/10.1007/s10032-015-0255-x

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