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<p>Automatic electrical and electronics component/symbol recognition from hand-drawn circuits is a challenging research problem. However, the literature survey reveals that there has been no significant progress in this domain. One possible reason for this might be a lack of publicly available datasets. To this end, in this work, we have developed a dataset, called JUHCCR-v1, which comprises 20 different hand-drawn circuit components that are commonly found in electrical and electronic circuits. Additionally, we have prepared a synthetic dataset having different variations (like orientations, stroke lengths, and distortions) of collected circuit components that may occur while extracting the components from an entire hand-drawn circuit diagram. This augmented dataset with the original ones would help train the deep learning based circuit component recognition algorithms. In order to provide a base result on this dataset, we have designed a weighted ensemble-based hand-drawn circuit component recognition method applied to snapshots of the convolutional block attention module-aided DenseNet-121 architecture. This benchmarking method achieves an accuracy of 91.15% on test set images. All datasets prepared here, along with codes, are made publicly available for the research community at: <a href="https://github.com/AyushRoy2001/Circuit-Component-Analysis">https://github.com/AyushRoy2001/Circuit-Component-Analysis</a>.</p>

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JUHCCR-v1: a database for hand-drawn electrical and electronics circuit component recognition

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月4日

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  • Ayush Roy,
  • Saptarshi Pani,
  • Samir Malakar,
  • Erik Cuevas,
  • Marco Pérez-Cisneros,
  • Ram Sarkar

摘要

Automatic electrical and electronics component/symbol recognition from hand-drawn circuits is a challenging research problem. However, the literature survey reveals that there has been no significant progress in this domain. One possible reason for this might be a lack of publicly available datasets. To this end, in this work, we have developed a dataset, called JUHCCR-v1, which comprises 20 different hand-drawn circuit components that are commonly found in electrical and electronic circuits. Additionally, we have prepared a synthetic dataset having different variations (like orientations, stroke lengths, and distortions) of collected circuit components that may occur while extracting the components from an entire hand-drawn circuit diagram. This augmented dataset with the original ones would help train the deep learning based circuit component recognition algorithms. In order to provide a base result on this dataset, we have designed a weighted ensemble-based hand-drawn circuit component recognition method applied to snapshots of the convolutional block attention module-aided DenseNet-121 architecture. This benchmarking method achieves an accuracy of 91.15% on test set images. All datasets prepared here, along with codes, are made publicly available for the research community at: https://github.com/AyushRoy2001/Circuit-Component-Analysis.