Hand Drawn Optical Circuit Recognition

Rabbani, Mahdi; Khoshkangini, Reza; Nagendraswamy, H. S.; Conti, Mauro

doi:10.1016/j.procs.2016.04.064

Cited by 24 publications

(15 citation statements)

References 9 publications

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“…What's more loses internal structure data about symbols [4,5]. [12], Electrical diagram is establishment of concentrates in electrical science. A circuit convey numerous data about the system.…”

Section: Introductionmentioning

confidence: 99%

KNN based Hand Drawn Electrical Circuit Recognition

Dewangan¹

2018

IJRASET

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Electrical diagram is foundation in electrical science. A circuit diagram pass on numerous data about the framework. Behind any gadget there are a lot of electrical fixings which play out their particular tasks, today all the electrical programming instruments neglected to successfully change over the data naturally from a circuit picture diagram to computerized shape. Henceforth electrical designers ought to physically enter all data into PCs, and this procedure requires some investment and carry mistakes with high likelihood. Besides, when the diagram is hand drawn, the issue is more convoluted for any electrical investigation. Accordingly, in this paper we propose another strategy utilizing K-Nearest Neighbour (KNN) to make a machine that can straightforwardly read the electrical images from a hand drawn circuit picture.

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Section: Introductionmentioning

confidence: 99%

KNN based Hand Drawn Electrical Circuit Recognition

Dewangan¹

2018

IJRASET

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“…The results indicate that the node recognition of 92% was achieved on a database comprising 107 nodes, and component recognition accuracy of 86% was achieved on a database containing 449 components. In 2016, Rabbani et al [29] investigated the recognition of electrical symbols from an electrical diagram using artificial neural networks. The process was divided into two phases: the first phase was feature extraction using shapebased features.…”

Section: Model Predictionmentioning

confidence: 99%

“…The artificial neural network was trained and tested with 20 different hand-drawn electrical images in each class. The authors concluded that the proposed method enables recognition and identification with high accuracy (Precision=85%, Recall=0.83%, and F-measure=0.83%) [29]. In 2016, Patare and Joshi [30] explored the method for sketched digital logic circuit components recognition.…”

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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“…The authors report 89% correct classification rate. In another recent study [14], authors employ image moments as features and train a feed forward artificial neural network to recognize a total of 31 circuit components, including digits, selected alphabets, units In [13], authors employ Fourier descriptors as features for recognition of digital logic circuit diagrams. Three components (AND, OR and NOT gates) were considered in the study with a total of 60 images.…”

Section: Related Workmentioning

confidence: 99%

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

Moetesum¹,

Younus²,

Warsi³

et al. 2018

ICST Transactions on Scalable Information Systems

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This paper presents an effective technique for segmentation and recognition of electronic components from hand-drawn circuit diagrams. Segmentation is carried out by using a series of morphological operations on the binarized images of circuits and discriminating between three categories of components (closed shape, components with connected lines, disconnected components). Each segmented component is characterized by computing the Histogram of Oriented Gradients (HOG) descriptor while classification is carried out using Support Vector Machine (SVM). The system is evaluated on 100 hand-drawn circuit diagrams with a total of 350 components. A segmentation accuracy of 87.7% while a classification rate of 92% is realized demonstrating the effectiveness of the proposed technique.

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Hand Drawn Optical Circuit Recognition

Cited by 24 publications

References 9 publications

KNN based Hand Drawn Electrical Circuit Recognition

KNN based Hand Drawn Electrical Circuit Recognition

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

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

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