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Offline hand-drawn circuit component recognition using texture and shape-based features

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Abstract

Circuit diagram is the very foundation of electrical and electronic sciences. A circuit diagram consists of various symbols called circuit components that specify the functionality of that circuit. Every day-to-day gadgets that we use are made up with a number of electrical/electronic circuits to play out their particular tasks. Till date circuit designers have to physically enter all data from the hand-drawn circuits into computers, and this procedure requires some investment in terms of time and carries mistakes with high likelihood. To this end, in this paper, we propose a method that relaxes this constraint by introducing a method for recognition of hand-drawn electrical and electronic circuit components, with both analog and digital components included. In the proposed method, the pre-processed images of circuit components are used for training and testing a recognition model using a feature set consisting of a texture based feature descriptor, called histogram of oriented gradients (HOG), and shape based features that include centroid distance, tangent angle, and chain code histogram. In addition, the texture based feature, being large in number compared to others is optimized using a feature selection algorithm called ReliefF. Classification of components is done by using sequential minimal optimization (SMO) classifier. The proposed method has been evaluated on a dataset of 20 different circuit components with 150 samples in each class. The experimental outcome shows that the proposed approach provides average 93.83% accuracy on the present database. We also compare our method with some of the state-of-the-art methods and we see that our method outperforms these methods.

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Acknowledgements

We would like to thank CMATER research laboratory of the Computer Science and Engineering Department, Jadavpur University, India for providing us the infrastructural support. This work is partially supported by the PURSE-II and UPE-II, Jadavpur University projects. Ram Sarkar is thankful to DST, Govt. of India, for the grant (EMR/2016/007213) to carry out this research.

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Authors and Affiliations

  1. Department of Electronics and Telecommunication, Jadavpur University, Kolkata, India

    Soham Roy, Archan Bhattacharya & Navonil Sarkar

  2. Department of Computer Science, Asutosh College, Kolkata, India

    Samir Malakar

  3. Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Ram Sarkar

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  1. Soham Roy
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  2. Archan Bhattacharya
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Correspondence to Samir Malakar.

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Roy, S., Bhattacharya, A., Sarkar, N. et al. Offline hand-drawn circuit component recognition using texture and shape-based features. Multimed Tools Appl 79, 31353–31373 (2020). https://doi.org/10.1007/s11042-020-09570-6

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  • DOI: https://doi.org/10.1007/s11042-020-09570-6

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