{"title":"基于个人笔迹身份分析的手写体字符识别算法研究[a]","authors":"P. Boribalburephan, B. Sakboonyarat","doi":"10.1109/KST.2012.6287732","DOIUrl":null,"url":null,"abstract":"The algorithm for online handwritten character recognition, PHIA algorithm, is introduced. The algorithm uses a likelihood score computed by a small neural network from every symbol pair for various decisions. Scores are used to generate a relationship map (Rivals/Non-rivals) between each symbol pairs. The training data is added to the database if and only if the relationship with the training data is `rival' for all existing database samples that identifies the same symbol. In the recognition phase, a nearest neighbor search is applied. During the search, if we traverse to a node whose relationship to the input is `non-rival', we later skip all processes that would operate on that node's rivals. This optimizes the decision path for each of the individual and enhances the ability to learn new symbols effectively.","PeriodicalId":209504,"journal":{"name":"Knowledge and Smart Technology (KST)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"An algorithm development for handwritten character recognition by personal handwriting identity analysis [PHIA]\",\"authors\":\"P. Boribalburephan, B. Sakboonyarat\",\"doi\":\"10.1109/KST.2012.6287732\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The algorithm for online handwritten character recognition, PHIA algorithm, is introduced. The algorithm uses a likelihood score computed by a small neural network from every symbol pair for various decisions. Scores are used to generate a relationship map (Rivals/Non-rivals) between each symbol pairs. The training data is added to the database if and only if the relationship with the training data is `rival' for all existing database samples that identifies the same symbol. In the recognition phase, a nearest neighbor search is applied. During the search, if we traverse to a node whose relationship to the input is `non-rival', we later skip all processes that would operate on that node's rivals. This optimizes the decision path for each of the individual and enhances the ability to learn new symbols effectively.\",\"PeriodicalId\":209504,\"journal\":{\"name\":\"Knowledge and Smart Technology (KST)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-07-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Knowledge and Smart Technology (KST)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/KST.2012.6287732\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knowledge and Smart Technology (KST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/KST.2012.6287732","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An algorithm development for handwritten character recognition by personal handwriting identity analysis [PHIA]
The algorithm for online handwritten character recognition, PHIA algorithm, is introduced. The algorithm uses a likelihood score computed by a small neural network from every symbol pair for various decisions. Scores are used to generate a relationship map (Rivals/Non-rivals) between each symbol pairs. The training data is added to the database if and only if the relationship with the training data is `rival' for all existing database samples that identifies the same symbol. In the recognition phase, a nearest neighbor search is applied. During the search, if we traverse to a node whose relationship to the input is `non-rival', we later skip all processes that would operate on that node's rivals. This optimizes the decision path for each of the individual and enhances the ability to learn new symbols effectively.