Home > Archive > 2019 > Volume 9 Number 3 (Jun. 2019) >
IJMLC 2019 Vol.9(3): 374-380 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.3.813

Handwritten Electric Circuit Diagram Recognition: An Approach Based on Finite State Machine

Lakshman Naika R, Dinesh R, and Prabhanjan S

Abstract—In this paper we propose a method for recognizing hand drawn electronic circuit diagrams. The proposed method first detect and classify each components present in the hand drawn circuit diagram. For the purpose of component recognition, we have constructed the feature vector by combining Local Binary Pattern (LBP) and statistical features based on pixel density. Classification of components is done by using support vector machine (SVM) classifier. Upon detection and recognition of components, the proposed method subsequently uses the position and sequence of arrangement of components to determine the type of circuit. For the purpose of establishing the sequence of components we have used finite state machine. The proposed method represents the sequence of recognized components as a string. This string representation of circuit is fed to a Finite State Machine (FSM) to detect type of circuit. The proposed method has been tested on about 100 hand written circuit diagrams of varying complexities and of different types. The proposed component detection method gives over 99% accuracy whereas, the circuit recognition method has recognition rate of over 85% recognition rate for the circuit type recognition.

Index Terms—Circuit recognition, finite state machine, Local binary pattern, SVM, statistical features.

Lakshman Naika R and Dinesh R are with Jain University, Bangalore, Karnataka (State), India (e-mail: laxubdt@gmail.com, dr.dinesh@gmail.com).
Prabhanjan S i s with Jyoti Institute of Technology, Bangalore, Karnataka (State), India (e-mail: prabhanjan_us@gmail.com).


Cite: Lakshman Naika R, Dinesh R, and Prabhanjan S, "Handwritten Electric Circuit Diagram Recognition: An Approach Based on Finite State Machine," International Journal of Machine Learning and Computing vol. 9, no. 3, pp. 374-380, 2019.

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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