Daniel Wilson-Nunn, Terry Lyons, A. Papavasiliou, Hao Ni
{"title":"A Path Signature Approach to Online Arabic Handwriting Recognition","authors":"Daniel Wilson-Nunn, Terry Lyons, A. Papavasiliou, Hao Ni","doi":"10.1109/ASAR.2018.8480300","DOIUrl":null,"url":null,"abstract":"The Arabic script is one that has many properties that come together and result in what is commonly cited as one of the most beautiful scripts. Used by over 400 million people worldwide and with a history spanning over 1800 years, the Arabic script remains one of the most important languages in the world. Using tools from the theory of rough paths, combined with state of the art techniques from deep learning, we develop a recognition methodology for Arabic handwriting. Preliminary results using online Arabic handwritten characters show that the methodology developed can result in a significant decrease in error rate.","PeriodicalId":165564,"journal":{"name":"2018 IEEE 2nd International Workshop on Arabic and Derived Script Analysis and Recognition (ASAR)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 2nd International Workshop on Arabic and Derived Script Analysis and Recognition (ASAR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASAR.2018.8480300","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13
Abstract
The Arabic script is one that has many properties that come together and result in what is commonly cited as one of the most beautiful scripts. Used by over 400 million people worldwide and with a history spanning over 1800 years, the Arabic script remains one of the most important languages in the world. Using tools from the theory of rough paths, combined with state of the art techniques from deep learning, we develop a recognition methodology for Arabic handwriting. Preliminary results using online Arabic handwritten characters show that the methodology developed can result in a significant decrease in error rate.