CircuitNet

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CircuitNet

Schematic Sketch to Circuit Diagram Using Deep Learning
Interactive Colab Demo »

Project Overview

A deep learning algorithm is proposed to automatically convert schematic sketches into circuit diagrams. The algorithm is promising, achieving a detection accuracy of 90% and a classification accuracy of 96.5%.

Component Segmentation

Classification Architecture

Software Dependancies

This project was built using the following open-source libraries:

• Numpy is an array manipulation library, used for linear algebra, Fourier transform, and random number capabilities.
• CV2 is a library for computer vision tasks.
• Skimage is a library which supports image processing applications on python.
• Matplotlib is a library which generates figures and provides graphical user interface toolkit.
• Tensorflow is an end-to-end open source machine learning platform
• SVG Schematic is a library to build a schematic using Python to instantiate and place the symbols and wires
• Cairo SVG is a library for processing SVG in python