{"title":"室内定位的文本和符号识别","authors":"Arpan Ghosh, Jeongwon Pyo, Tae-Yong Kuc","doi":"10.23919/ICCAS50221.2020.9268328","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a modular approach to estimate the position and rotation of any mobile robot more precisely in an indoor environment using text and sign recognition. The modular approach for the text and sign recognition is performed in a twofold method in figure 1. First is the detection of the region with texts and various signs in the image which is done by an object detection system. The second part is the character recognition, where the detected textual region from the image will be passed onto an optical character recognition engine(OCR) engine to be recognized. This modular approach can be modified at any point based on any mobile robot in an indoor environment with texts and signs to help localize its position and rotation.","PeriodicalId":6732,"journal":{"name":"2020 20th International Conference on Control, Automation and Systems (ICCAS)","volume":"1 1","pages":"1006-1009"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Text and Sign Recognition for Indoor Localization\",\"authors\":\"Arpan Ghosh, Jeongwon Pyo, Tae-Yong Kuc\",\"doi\":\"10.23919/ICCAS50221.2020.9268328\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a modular approach to estimate the position and rotation of any mobile robot more precisely in an indoor environment using text and sign recognition. The modular approach for the text and sign recognition is performed in a twofold method in figure 1. First is the detection of the region with texts and various signs in the image which is done by an object detection system. The second part is the character recognition, where the detected textual region from the image will be passed onto an optical character recognition engine(OCR) engine to be recognized. This modular approach can be modified at any point based on any mobile robot in an indoor environment with texts and signs to help localize its position and rotation.\",\"PeriodicalId\":6732,\"journal\":{\"name\":\"2020 20th International Conference on Control, Automation and Systems (ICCAS)\",\"volume\":\"1 1\",\"pages\":\"1006-1009\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 20th International Conference on Control, Automation and Systems (ICCAS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/ICCAS50221.2020.9268328\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 20th International Conference on Control, Automation and Systems (ICCAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/ICCAS50221.2020.9268328","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, we propose a modular approach to estimate the position and rotation of any mobile robot more precisely in an indoor environment using text and sign recognition. The modular approach for the text and sign recognition is performed in a twofold method in figure 1. First is the detection of the region with texts and various signs in the image which is done by an object detection system. The second part is the character recognition, where the detected textual region from the image will be passed onto an optical character recognition engine(OCR) engine to be recognized. This modular approach can be modified at any point based on any mobile robot in an indoor environment with texts and signs to help localize its position and rotation.
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