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

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.

An Empirical Investigation of the Effectiveness of Optical Recognition of Hand-Drawn Business Process Elements by Applying Machine Learning

“…For example, only two studies [37], [38] report a higher accuracy rate than the accuracy rate for recognizing the BPMN element 'Exclusive gateway', which achieved a recognition accuracy of 95%. The recognition rates of BPMN elements 'Non-interrupting start time event' and 'User task' are slightly lower but comparable with results presented in [26], [34], [35], [36], while the accuracy rate for recognition of element 'Complex gateway' is comparable to the results presented in research [30] and [31]. Unfortunately, the results of the remaining investigated BPMN elements indicated a lower accuracy rate (< 76%) when compared to similar studies.…”

“…Nevertheless, some general parallels between the results of our work and related research can be made. In general, the average accuracy rate (61%) of all investigated BPMN elements is lower compared to similar research [26], [27], [28], [30], [31], [34], [36], [37], [38]. In the related research, the lowest accuracy rate was identified in research [27], where the system correctly identified 79% of the UML symbols.…”

“…The results show a segmentation accuracy of 87.7% and a classification rate of 92%. In 2019, Lakshman et al [31] investigated a method for the recognition of hand-drawn electronic circuit diagrams, based on (1) feature vector by combining Local Binary Pattern (LBP) and statistical features based on pixel density for component recognition; (2) support vector machine (SVM) for classification of components and; (3) the position and sequence of arrangement of components to determine the type of circuit. The results showed that the component recognition method achieved recognition rate of over 99% whereas the circuit type recognition method had a recognition rate above 85%.…”

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“…The research efforts in this domain are not limited to digital circuits but they have been extended to recognize electrical circuits [15][16][17]. The authors in previous studies [15,16] applied image segmentation and feature extraction of electrical circuit elements (e.g., resistor and capacitor). The extracted features included the histogram of oriented gradients, local binary pattern (LBP), statistical features based on pixel density like scalar pixel-distribution, and vector relationships between straight lines in polygonal representations.…”

“…The SVM, DT, and KNN machine learning algorithms used in the previously proposed techniques require careful and accurate feature selection and extraction in order to achieve acceptable recognition results. However, manually extracting the features as done previously [13][14][15][16][17] requires deep analysis of target elements. In addition, for each new digital block or circuit element the feature extraction procedure might be repeated with new features required to resolve conflicts and similarities.…”