Massive Figure Extraction and Classification in Electronic Component Datasheets for Accelerating PCB Design Preparation

Kuan Chun Chen; Chou Chen Lee; Po-Hung Lin; Yan Jhih Wang; Yi Ting Chen

doi:10.1109/MLCAD52597.2021.9531275

Massive Figure Extraction and Classification in Electronic Component Datasheets for Accelerating PCB Design Preparation

Kuan Chun Chen, Chou Chen Lee, Po-Hung Lin, Yan Jhih Wang, Yi Ting Chen

Institute of Intelligent Systems

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

2 Scopus citations

Abstract

Before starting printed-circuit-board (PCB) design, it is usually very time-consuming for PCB and system designers to review a large amount of electronic component datasheets in order to determine the best integration of electronic components for the target electronic systems. Each datasheet may contain over hundred figures and tables, while the figures and tables usually present the most important electronic component specifications. This paper categorizes various figures, including tables, in electronic component datasheets, and proposes the ECS-YOLO model for massive figure extraction and classification in order to accelerate PCB design preparation process. The experimental results show that, compared with the state-of-the-art object detection model, the proposed ECS-YOLO can consistently achieve better accuracy for figure extraction and classification in electronic component datasheets.

Original language	English
Title of host publication	2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD, MLCAD 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665431668
DOIs	https://doi.org/10.1109/MLCAD52597.2021.9531275
State	Published - 30 Aug 2021
Event	3rd ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2021 - Raleigh, United States Duration: 30 Aug 2021 → 3 Sep 2021

Publication series

Name	2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD, MLCAD 2021

Conference

Conference	3rd ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2021
Country/Territory	United States
City	Raleigh
Period	30/08/21 → 3/09/21

Access to Document

10.1109/MLCAD52597.2021.9531275

Cite this

Chen, K. C., Lee, C. C., Lin, P.-H., Wang, Y. J., & Chen, Y. T. (2021). Massive Figure Extraction and Classification in Electronic Component Datasheets for Accelerating PCB Design Preparation. In 2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD, MLCAD 2021 (2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD, MLCAD 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MLCAD52597.2021.9531275

Chen, Kuan Chun ; Lee, Chou Chen ; Lin, Po-Hung et al. / Massive Figure Extraction and Classification in Electronic Component Datasheets for Accelerating PCB Design Preparation. 2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD, MLCAD 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD, MLCAD 2021).

@inproceedings{2718efcdbd0d426e8434f7b9837bc039,

title = "Massive Figure Extraction and Classification in Electronic Component Datasheets for Accelerating PCB Design Preparation",

abstract = "Before starting printed-circuit-board (PCB) design, it is usually very time-consuming for PCB and system designers to review a large amount of electronic component datasheets in order to determine the best integration of electronic components for the target electronic systems. Each datasheet may contain over hundred figures and tables, while the figures and tables usually present the most important electronic component specifications. This paper categorizes various figures, including tables, in electronic component datasheets, and proposes the ECS-YOLO model for massive figure extraction and classification in order to accelerate PCB design preparation process. The experimental results show that, compared with the state-of-the-art object detection model, the proposed ECS-YOLO can consistently achieve better accuracy for figure extraction and classification in electronic component datasheets.",

author = "Chen, {Kuan Chun} and Lee, {Chou Chen} and Po-Hung Lin and Wang, {Yan Jhih} and Chen, {Yi Ting}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 3rd ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2021 ; Conference date: 30-08-2021 Through 03-09-2021",

year = "2021",

month = aug,

day = "30",

doi = "10.1109/MLCAD52597.2021.9531275",

language = "English",

series = "2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD, MLCAD 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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address = "美國",

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Chen, KC, Lee, CC, Lin, P-H, Wang, YJ & Chen, YT 2021, Massive Figure Extraction and Classification in Electronic Component Datasheets for Accelerating PCB Design Preparation. in 2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD, MLCAD 2021. 2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD, MLCAD 2021, Institute of Electrical and Electronics Engineers Inc., 3rd ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2021, Raleigh, United States, 30/08/21. https://doi.org/10.1109/MLCAD52597.2021.9531275

Massive Figure Extraction and Classification in Electronic Component Datasheets for Accelerating PCB Design Preparation. / Chen, Kuan Chun; Lee, Chou Chen; Lin, Po-Hung et al.
2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD, MLCAD 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD, MLCAD 2021).

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

TY - GEN

T1 - Massive Figure Extraction and Classification in Electronic Component Datasheets for Accelerating PCB Design Preparation

AU - Chen, Kuan Chun

AU - Lee, Chou Chen

AU - Lin, Po-Hung

AU - Wang, Yan Jhih

AU - Chen, Yi Ting

PY - 2021/8/30

Y1 - 2021/8/30

N2 - Before starting printed-circuit-board (PCB) design, it is usually very time-consuming for PCB and system designers to review a large amount of electronic component datasheets in order to determine the best integration of electronic components for the target electronic systems. Each datasheet may contain over hundred figures and tables, while the figures and tables usually present the most important electronic component specifications. This paper categorizes various figures, including tables, in electronic component datasheets, and proposes the ECS-YOLO model for massive figure extraction and classification in order to accelerate PCB design preparation process. The experimental results show that, compared with the state-of-the-art object detection model, the proposed ECS-YOLO can consistently achieve better accuracy for figure extraction and classification in electronic component datasheets.

AB - Before starting printed-circuit-board (PCB) design, it is usually very time-consuming for PCB and system designers to review a large amount of electronic component datasheets in order to determine the best integration of electronic components for the target electronic systems. Each datasheet may contain over hundred figures and tables, while the figures and tables usually present the most important electronic component specifications. This paper categorizes various figures, including tables, in electronic component datasheets, and proposes the ECS-YOLO model for massive figure extraction and classification in order to accelerate PCB design preparation process. The experimental results show that, compared with the state-of-the-art object detection model, the proposed ECS-YOLO can consistently achieve better accuracy for figure extraction and classification in electronic component datasheets.

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DO - 10.1109/MLCAD52597.2021.9531275

M3 - Conference contribution

AN - SCOPUS:85115726554

T3 - 2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD, MLCAD 2021

BT - 2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD, MLCAD 2021

PB - Institute of Electrical and Electronics Engineers Inc.

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Chen KC, Lee CC, Lin PH, Wang YJ, Chen YT. Massive Figure Extraction and Classification in Electronic Component Datasheets for Accelerating PCB Design Preparation. In 2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD, MLCAD 2021. Institute of Electrical and Electronics Engineers Inc. 2021. (2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD, MLCAD 2021). doi: 10.1109/MLCAD52597.2021.9531275

Massive Figure Extraction and Classification in Electronic Component Datasheets for Accelerating PCB Design Preparation

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