Deep-learning-based Extraction of Electronic Component Parameters from Datasheets

Tzung Pei Hong; Hsiu Wei Chiu; Shih Feng Huang; Yi Ting Chen

doi:10.1109/BigData52589.2021.9672035

Deep-learning-based Extraction of Electronic Component Parameters from Datasheets

Tzung Pei Hong, Hsiu Wei Chiu, Shih Feng Huang, Yi Ting Chen

Graduate Institute of Statistics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

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.

Original language	English
Title of host publication	Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
Editors	Yixin Chen, Heiko Ludwig, Yicheng Tu, Usama Fayyad, Xingquan Zhu, Xiaohua Tony Hu, Suren Byna, Xiong Liu, Jianping Zhang, Shirui Pan, Vagelis Papalexakis, Jianwu Wang, Alfredo Cuzzocrea, Carlos Ordonez
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5501-5506
Number of pages	6
ISBN (Electronic)	9781665439022
DOIs	https://doi.org/10.1109/BigData52589.2021.9672035
State	Published - 2021
Event	2021 IEEE International Conference on Big Data, Big Data 2021 - Virtual, Online, United States Duration: 15 Dec 2021 → 18 Dec 2021

Publication series

Name	Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021

Conference

Conference	2021 IEEE International Conference on Big Data, Big Data 2021
Country/Territory	United States
City	Virtual, Online
Period	15/12/21 → 18/12/21

Keywords

deep learning
electronic design automation
object detection
text extraction
three-view drawing

Access to Document

10.1109/BigData52589.2021.9672035

Cite this

Hong, T. P., Chiu, H. W., Huang, S. F., & Chen, Y. T. (2021). Deep-learning-based Extraction of Electronic Component Parameters from Datasheets. In Y. Chen, H. Ludwig, Y. Tu, U. Fayyad, X. Zhu, X. T. Hu, S. Byna, X. Liu, J. Zhang, S. Pan, V. Papalexakis, J. Wang, A. Cuzzocrea, & C. Ordonez (Eds.), Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021 (pp. 5501-5506). (Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData52589.2021.9672035

Hong, Tzung Pei ; Chiu, Hsiu Wei ; Huang, Shih Feng et al. / Deep-learning-based Extraction of Electronic Component Parameters from Datasheets. Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021. editor / Yixin Chen ; Heiko Ludwig ; Yicheng Tu ; Usama Fayyad ; Xingquan Zhu ; Xiaohua Tony Hu ; Suren Byna ; Xiong Liu ; Jianping Zhang ; Shirui Pan ; Vagelis Papalexakis ; Jianwu Wang ; Alfredo Cuzzocrea ; Carlos Ordonez. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 5501-5506 (Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021).

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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",

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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",

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Hong, TP, Chiu, HW, Huang, SF & Chen, YT 2021, Deep-learning-based Extraction of Electronic Component Parameters from Datasheets. in Y Chen, H Ludwig, Y Tu, U Fayyad, X Zhu, XT Hu, S Byna, X Liu, J Zhang, S Pan, V Papalexakis, J Wang, A Cuzzocrea & C Ordonez (eds), Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021. Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021, Institute of Electrical and Electronics Engineers Inc., pp. 5501-5506, 2021 IEEE International Conference on Big Data, Big Data 2021, Virtual, Online, United States, 15/12/21. https://doi.org/10.1109/BigData52589.2021.9672035

Deep-learning-based Extraction of Electronic Component Parameters from Datasheets. / Hong, Tzung Pei; Chiu, Hsiu Wei; Huang, Shih Feng et al.
Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021. ed. / Yixin Chen; Heiko Ludwig; Yicheng Tu; Usama Fayyad; Xingquan Zhu; Xiaohua Tony Hu; Suren Byna; Xiong Liu; Jianping Zhang; Shirui Pan; Vagelis Papalexakis; Jianwu Wang; Alfredo Cuzzocrea; Carlos Ordonez. Institute of Electrical and Electronics Engineers Inc., 2021. p. 5501-5506 (Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Deep-learning-based Extraction of Electronic Component Parameters from Datasheets

AU - Hong, Tzung Pei

AU - Chiu, Hsiu Wei

AU - Huang, Shih Feng

AU - Chen, Yi Ting

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - deep learning

KW - electronic design automation

KW - object detection

KW - text extraction

KW - three-view drawing

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U2 - 10.1109/BigData52589.2021.9672035

DO - 10.1109/BigData52589.2021.9672035

M3 - 會議論文篇章

AN - SCOPUS:85125306507

T3 - Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021

SP - 5501

EP - 5506

BT - Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021

A2 - Chen, Yixin

A2 - Ludwig, Heiko

A2 - Tu, Yicheng

A2 - Fayyad, Usama

A2 - Zhu, Xingquan

A2 - Hu, Xiaohua Tony

A2 - Byna, Suren

A2 - Liu, Xiong

A2 - Zhang, Jianping

A2 - Pan, Shirui

A2 - Papalexakis, Vagelis

A2 - Wang, Jianwu

A2 - Cuzzocrea, Alfredo

A2 - Ordonez, Carlos

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE International Conference on Big Data, Big Data 2021

Y2 - 15 December 2021 through 18 December 2021

ER -

Hong TP, Chiu HW, Huang SF, Chen YT. Deep-learning-based Extraction of Electronic Component Parameters from Datasheets. In Chen Y, Ludwig H, Tu Y, Fayyad U, Zhu X, Hu XT, Byna S, Liu X, Zhang J, Pan S, Papalexakis V, Wang J, Cuzzocrea A, Ordonez C, editors, Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 5501-5506. (Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021). doi: 10.1109/BigData52589.2021.9672035

Deep-learning-based Extraction of Electronic Component Parameters from Datasheets

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