Abstract
With the objective of creating an interface for experimenting with electronic circuits embedded in documents or images, in this paper we have presented a system for parsing and understanding of electronic circuit diagrams. The developed system consists of following steps- symbol extraction, symbol recognition, optimization and netlist-representation. Firstly, symbols are extracted from the image by removing text and connection lines using computer vision techniques. For symbol recognizer a probabilistic-SVM classifier is built using HOG and radon features on training data. A Bayesian framework is used to incorporate domain knowledge information to improve the performance of the probabilistic symbol recognizer. An novel optimization approach based on top-down features is used to remove the errors that occurs in the symbol extraction and recognition task. A depth first traversal algorithm is used to find the connections between the symbols and then image is represented in the form of usable data structure. The system is evaluated on a dataset of 20 analog electronic circuit images collected from various sources and the results are presented.
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Agarwal, S., Agrawal, M., Chaudhury, S. (2017). Recognizing Electronic Circuits to Enrich Web Documents for Electronic Simulation. In: Lamiroy, B., Dueire Lins, R. (eds) Graphic Recognition. Current Trends and Challenges. GREC 2015. Lecture Notes in Computer Science(), vol 9657. Springer, Cham. https://doi.org/10.1007/978-3-319-52159-6_5
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