{"title":"基于LabVIEW的字符识别系统设计","authors":"Jiaodi Liu, Yulong Duan","doi":"10.1145/3480571.3480622","DOIUrl":null,"url":null,"abstract":"∗Aiming at the low degree of automation in the recognition of paper documents, intelligent instruments, license plates and other display contents, a machine Vision system for character recognition is developed on LabVIEW virtual instrument platform by using IMAQ Vision toolkit. The system uses the computer’s own camera for image acquisition, and carries out grayscale, binarization, enhancement and denoising of images. Finally, optical character recognition (OCR) provided by NI Vision Assistant is used for character segmentation, training and recognition. The recognition experiments on paper documents and license plates show that the recognition success rate of the system is 96.3%, and the recognition speed is fast, so it is feasible.","PeriodicalId":113723,"journal":{"name":"Proceedings of the 6th International Conference on Intelligent Information Processing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Design of character recognition system based on LabVIEW\",\"authors\":\"Jiaodi Liu, Yulong Duan\",\"doi\":\"10.1145/3480571.3480622\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"∗Aiming at the low degree of automation in the recognition of paper documents, intelligent instruments, license plates and other display contents, a machine Vision system for character recognition is developed on LabVIEW virtual instrument platform by using IMAQ Vision toolkit. The system uses the computer’s own camera for image acquisition, and carries out grayscale, binarization, enhancement and denoising of images. Finally, optical character recognition (OCR) provided by NI Vision Assistant is used for character segmentation, training and recognition. The recognition experiments on paper documents and license plates show that the recognition success rate of the system is 96.3%, and the recognition speed is fast, so it is feasible.\",\"PeriodicalId\":113723,\"journal\":{\"name\":\"Proceedings of the 6th International Conference on Intelligent Information Processing\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-07-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 6th International Conference on Intelligent Information Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3480571.3480622\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 6th International Conference on Intelligent Information Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3480571.3480622","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Design of character recognition system based on LabVIEW
∗Aiming at the low degree of automation in the recognition of paper documents, intelligent instruments, license plates and other display contents, a machine Vision system for character recognition is developed on LabVIEW virtual instrument platform by using IMAQ Vision toolkit. The system uses the computer’s own camera for image acquisition, and carries out grayscale, binarization, enhancement and denoising of images. Finally, optical character recognition (OCR) provided by NI Vision Assistant is used for character segmentation, training and recognition. The recognition experiments on paper documents and license plates show that the recognition success rate of the system is 96.3%, and the recognition speed is fast, so it is feasible.
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