{"title":"Multiple Model Kalman Filter Approach for Show-through Cancellation","authors":"Sabita Langkam, A. K. Deb","doi":"10.1109/ASAR.2018.8480311","DOIUrl":"https://doi.org/10.1109/ASAR.2018.8480311","url":null,"abstract":"Documents of historical importance and other duplex printed ones often suffer degradation due to ink on one side seeping into the other side and/or contents on one side appearing and interfering with contents on the other side. These are respectively called as bleed-through and show-through. A dual estimation approach is proposed in the paper that uses both the recto side of the document and the verso side for show-through cancellation. The main assumption is that the degradation is because of the linear mixing of the recto (verso) image and horizontally flipped verso (recto) image. The state-space model framed to define the show-through can be used to estimate the states which are clear recto and verso images. The proposed method uses multiple Kalman filters to solve for state estimation and deal with unknown parameters in the model. The simulation results for different cases establish the validity of the proposed approach.","PeriodicalId":165564,"journal":{"name":"2018 IEEE 2nd International Workshop on Arabic and Derived Script Analysis and Recognition (ASAR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123297228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abdulaziz M. Alayba, V. Palade, M. England, R. Iqbal
{"title":"Improving Sentiment Analysis in Arabic Using Word Representation","authors":"Abdulaziz M. Alayba, V. Palade, M. England, R. Iqbal","doi":"10.1109/ASAR.2018.8480191","DOIUrl":"https://doi.org/10.1109/ASAR.2018.8480191","url":null,"abstract":"The complexities of Arabic language in morphology, orthography and dialects makes sentiment analysis for Arabic more challenging. Also, text feature extraction from short messages like tweets, in order to gauge the sentiment, makes this task even more difficult. In recent years, deep neural networks were often employed and showed very good results in sentiment classification and natural language processing applications. Word embedding, or word distributing approach, is a current and powerful tool to capture together the closest words from a contextual text.In this paper, we describe how we construct Word2Vec models from a large Arabic corpus obtained from ten newspapers in different Arab countries. By applying different machine learning algorithms and convolutional neural networks with different text feature selections, we report improved accuracy of sentiment classification (91%-95%) on our publicly available Arabic language health sentiment dataset [1].","PeriodicalId":165564,"journal":{"name":"2018 IEEE 2nd International Workshop on Arabic and Derived Script Analysis and Recognition (ASAR)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117203443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}