Digitizing images of electrical-circuit schematics
Electrical-circuit schematics are a foundational tool in electrical engineering. A method that can automatically digitalize them is desirable since a knowledge base of such schematics could preserve their functional information as well as provide a database that one can mine to predict more operatio...
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| Vydáno v: | APL machine learning Ročník 2; číslo 1; s. 016109 - 016109-10 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
AIP Publishing LLC
01.03.2024
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| ISSN: | 2770-9019, 2770-9019 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | Electrical-circuit schematics are a foundational tool in electrical engineering. A method that can automatically digitalize them is desirable since a knowledge base of such schematics could preserve their functional information as well as provide a database that one can mine to predict more operationally efficient electrical circuits using data analytics and machine learning. We present a workflow that contains a novel pattern-recognition methodology and a custom-trained Optical Character Recognition (OCR) model that can digitalize images of electrical-circuit schematics with minimal configuration. The pattern-recognition and OCR stages of the workflow yield 86.4% and 99.6% success rates, respectively. We also present an extendable option toward predictive circuit-design efficiencies, subject to a large database of images being available. Thereby, data gathered from our pattern-recognition workflow are used to draw network graphs, which are in turn employed to form matrix equations that contain the voltages and currents for all nodes in the circuit in terms of component values. These equations could be applied to a database of electrical-circuit schematics to predict new circuit designs or circuit modifications that offer greater operational efficiency. Alternatively, these network graphs could be converted into simulation programs with integrated circuit emphasis netlists to afford more accurate and computationally automated simulations. |
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| ISSN: | 2770-9019 2770-9019 |
| DOI: | 10.1063/5.0177755 |