Segmentation and Recognition of Electronic Components in Hand-Drawn Circuit Diagrams

Moetesum, Momina; Younus, Syed Waqar; Warsi, M; Siddiqi, Imran

doi:10.4108/eai.13-4-2018.154478

Cited by 17 publications

(19 citation statements)

References 10 publications

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“…For example, Du et al [15] combined the least squares method with the Hough transform and classified components by extracting the edge features of components [16][17][18]. Although the classification accuracy is relatively high, traditional image classification methods cannot process very large images [19], the computational complexity is prohibitive, and it is difficult to achieve multiple component classification at the same time.…”

Section: Traditional Image Classification Methodsmentioning

confidence: 99%

An Electronic Component Recognition Algorithm Based on Deep Learning with a Faster SqueezeNet

Yang

Luo

et al. 2020

Mathematical Problems in Engineering

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Electronic component recognition plays an important role in industrial production, electronic manufacturing, and testing. In order to address the problem of the low recognition recall and accuracy of traditional image recognition technologies (such as principal component analysis (PCA) and support vector machine (SVM)), this paper selects multiple deep learning networks for testing and optimizes the SqueezeNet network. The paper then presents an electronic component recognition algorithm based on the Faster SqueezeNet network. This structure can reduce the size of network parameters and computational complexity without deteriorating the performance of the network. The results show that the proposed algorithm performs well, where the Receiver Operating Characteristic Curve (ROC) and Area Under the Curve (AUC), capacitor and inductor, reach 1.0. When the FPR is less than or equal 10 − 6 level, the TPR is greater than or equal to 0.99; its reasoning time is about 2.67 ms, achieving the industrial application level in terms of time consumption and performance.

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Section: Traditional Image Classification Methodsmentioning

confidence: 99%

An Electronic Component Recognition Algorithm Based on Deep Learning with a Faster SqueezeNet

Yang

Luo

et al. 2020

Mathematical Problems in Engineering

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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.…”

Section: Online Recognitionmentioning

confidence: 99%

“…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.…”

Section: Online Recognitionmentioning

confidence: 99%

Sketic: a machine learning-based digital circuit recognition platform

Abdel-Majeed¹,

Almousa²,

Alsalman³

et al. 2020

Turk J Elec Eng & Comp Sci

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In digital system design, digital logic circuit diagrams are built using interconnects and symbolic representations of the basic logic gates. Constructing such diagrams using free sketches is the first step in the design process.After that the circuit schematic or code has to be generated before being able to simulate the design. While most of the mentioned steps are automated using design automation tools, drafting the schematic circuit and then converting it into a valid format that can be simulated are still done manually due to the lack of robust tools that can recognize the free sketches and incorporate them into end user simulators. Hence, the goal of this paper is to construct and deploy computer simulation tools capable of understanding free sketches and incorporate them into useful simulation tools.Such a tool will be useful at both the educational and the industrial levels. Moreover, while this tool is designed to deal with sketched logic circuits, it can be generalized and applied to many other fields to convert the sketched design into a digital format. To implement this tool, we relied on the emerging machine learning and image processing concepts to make sure that the designed system is robust and accurate. Our results show that our system is able to recognize all the gates in the digital circuit with more than 95% accuracy.

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“…Within that, the researchers investigated the recognition of different handdrawn graphical artifacts, including electronic circuits and components, UML diagrams, architectural drawings, and flow charts. [25]. For example, in 2002, Wenyin et al [26] investigated on-line sketchy graphics recognition.…”

Section: Model Predictionmentioning

confidence: 99%

“…The results indicate an achievement of an average of 83% of the circuit recognition accuracy. In 2018, Moetesum et al [25] presented a technique for the segmentation and recognition of electronic components given with hand-drawn circuit diagrams. The segmentation used a set of morphological operations on the binarized images of circuits.…”

Section: Model Predictionmentioning

confidence: 99%

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

Polančič

Jagecic²,

Kous

2020

IEEE Access

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Business process diagrams represent an essential part of business process management activities, including process analysis, process-related communication, and process automation. While the majority of these artifacts are produced with modeling tools, there are still cases where hand-drawn diagrams are created, especially in the initial phases of process discovery and process innovation activities. The transformation of a hand-drawn diagram to digital format often presents several challenges. Thus, our efforts are directed towards investigating the effectiveness of an automatic transformation of hand-drawn diagrams into digital artifacts utilizing optical character recognition. To this aim, we performed empirical research in which subjects were instructed to redraw standardized process elements, which were afterward used for the training and testing set of machine-learning-based character recognition application. The findings obtained in the analysis showed that TensorFlow based and trained solution is capable of identifying hand-drawn process diagrams elements on different levels of accuracy. These insights may be considered when specifying or adapting the visual vocabularies of notations assuring appropriate visual distances between depictions of individual elements. INDEX TERMS optical character recognition, business process diagram, hand-drawing, machine learning, BPMN.

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Segmentation and Recognition of Electronic Components in Hand-Drawn Circuit Diagrams

Cited by 17 publications

References 10 publications

An Electronic Component Recognition Algorithm Based on Deep Learning with a Faster SqueezeNet

An Electronic Component Recognition Algorithm Based on Deep Learning with a Faster SqueezeNet

Sketic: a machine learning-based digital circuit recognition platform

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

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