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

研究成果: Conference contribution同行評審

摘要

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.

原文English
主出版物標題2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD, MLCAD 2021
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781665431668
DOIs
出版狀態Published - 30 8月 2021
事件3rd ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2021 - Raleigh, United States
持續時間: 30 8月 20213 9月 2021

出版系列

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

Conference

Conference3rd ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2021
國家/地區United States
城市Raleigh
期間30/08/213/09/21

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