@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.; null ; 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.",
booktitle = "2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD, MLCAD 2021",
address = "United States",
}