@inproceedings{4d3521c76dc044468490893f18931143,
title = "Deep-learning-based Extraction of Electronic Component Parameters from Datasheets",
abstract = "In this paper, we propose an automatic extraction process of the dimension parameters shown in three-view drawings. It is divided into two stages. In the first stage, we detect three-view drawings in datasheets and find out the text regions containing the parameters in the drawings by deep learning. We then recognize the values in these regions. In the second stage, we design two algorithms, based on k-nearest neighbors (k-NN) and statistical evaluation, respectively, to match the digitized parameters with the values. We also conduct experiments to show the high accuracy in the two stages.",
keywords = "deep learning, electronic design automation, object detection, text extraction, three-view drawing",
author = "Hong, {Tzung Pei} and Chiu, {Hsiu Wei} and Huang, {Shih Feng} and Chen, {Yi Ting}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Big Data, Big Data 2021 ; Conference date: 15-12-2021 Through 18-12-2021",
year = "2021",
doi = "10.1109/BigData52589.2021.9672035",
language = "???core.languages.en_GB???",
series = "Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5501--5506",
editor = "Yixin Chen and Heiko Ludwig and Yicheng Tu and Usama Fayyad and Xingquan Zhu and Hu, {Xiaohua Tony} and Suren Byna and Xiong Liu and Jianping Zhang and Shirui Pan and Vagelis Papalexakis and Jianwu Wang and Alfredo Cuzzocrea and Carlos Ordonez",
booktitle = "Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021",
}