U. Hayat, Muhammad Aatif, Osama Zeeshan, I. Siddiqi
{"title":"Ligature Recognition in Urdu Caption Text using Deep Convolutional Neural Networks","authors":"U. Hayat, Muhammad Aatif, Osama Zeeshan, I. Siddiqi","doi":"10.1109/ICET.2018.8603586","DOIUrl":null,"url":null,"abstract":"Textual content in videos contain rich information that can be exploited for semantic indexing and subsequent retrieval as well as development of video analytics solutions. The key modules in a textual content based video retrieval system include detection (localization) of text followed by its recognition, the later being the subject of our study. More specifically, this paper presents a caption text recognition system targeting Urdu text. The technique relies on a holistic approach using ligatures as units of recognition. Data driven feature extraction techniques are employed using a number of pre-trained deep convolution neural networks. The networks are used as feature extractors as well as fine-tuned on the ligature dataset under study and realized high ligature recognition rates.","PeriodicalId":443353,"journal":{"name":"2018 14th International Conference on Emerging Technologies (ICET)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 14th International Conference on Emerging Technologies (ICET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICET.2018.8603586","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10
Abstract
Textual content in videos contain rich information that can be exploited for semantic indexing and subsequent retrieval as well as development of video analytics solutions. The key modules in a textual content based video retrieval system include detection (localization) of text followed by its recognition, the later being the subject of our study. More specifically, this paper presents a caption text recognition system targeting Urdu text. The technique relies on a holistic approach using ligatures as units of recognition. Data driven feature extraction techniques are employed using a number of pre-trained deep convolution neural networks. The networks are used as feature extractors as well as fine-tuned on the ligature dataset under study and realized high ligature recognition rates.