{"title":"电子元件数据表中海量图形的提取与分类,加速PCB设计准备","authors":"Kuan-Chun Chen, Chou-Chen Lee, Mark Po-Hung Lin, Yan-Jhih Wang, Yi-Ting Chen","doi":"10.1109/MLCAD52597.2021.9531275","DOIUrl":null,"url":null,"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.","PeriodicalId":210763,"journal":{"name":"2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Massive Figure Extraction and Classification in Electronic Component Datasheets for Accelerating PCB Design Preparation\",\"authors\":\"Kuan-Chun Chen, Chou-Chen Lee, Mark Po-Hung Lin, Yan-Jhih Wang, Yi-Ting Chen\",\"doi\":\"10.1109/MLCAD52597.2021.9531275\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":210763,\"journal\":{\"name\":\"2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MLCAD52597.2021.9531275\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MLCAD52597.2021.9531275","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Massive Figure Extraction and Classification in Electronic Component Datasheets for Accelerating PCB Design Preparation
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.