CompSciRN: Audio最新文献

CompSciRN: Audio Pub Date : 2020-12-25 DOI: 10.2139/ssrn.3755321

Steven Strauss

{"title":"Teaching Case: Should New York City (NYC) Build the Brooklyn Queens Connector (BQX) or Will it Soon Be Obsolete?","authors":"Steven Strauss","doi":"10.2139/ssrn.3755321","DOIUrl":"https://doi.org/10.2139/ssrn.3755321","url":null,"abstract":"Marc Andreessen (a co-founder and general partner of venture capital firm Andreessen-Horowitz, who was an early stage investor – either personally or via his firm - in Facebook, Groupon, Skype, Twitter, Zynga, Foursquare, LinkedIn and others) famously observed that software is eating the world, by which he means that every industry sector is being impacted by the confluence of ever declining computing and digital storage costs, combined with continual improvements in software. In the private sector the result has been the rise of firms like Google, Amazon, Facebook, Uber and so on, with the simultaneous devastation of many traditional organizations (e.g., physical retail bookstore chains such as Borders Books, and/or many local newspapers) that were made effectively obsolete. We often think of this as a private sector issue, perhaps only impacting the public sector in terms of thinking about work force retraining and similar activities, but in truth this wave of obsolescence has implications for the public sector in areas we don’t normally associate with digital technology. For example, the rise of autonomous vehicles, and related technologies, may have important implications for the feasibility of big transport infrastructure projects, such as the BQX. <br>","PeriodicalId":145147,"journal":{"name":"CompSciRN: Audio","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133808009","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}

引用次数: 0

Text Region Detection and Recognition in Natural Scene Images Using MSER and Convolutional Neural Network 基于MSER和卷积神经网络的自然场景图像文本区域检测与识别

CompSciRN: Audio Pub Date : 2020-11-21 DOI: 10.2139/ssrn.3734809

A. V, S. M, T. V

{"title":"Text Region Detection and Recognition in Natural Scene Images Using MSER and Convolutional Neural Network","authors":"A. V, S. M, T. V","doi":"10.2139/ssrn.3734809","DOIUrl":"https://doi.org/10.2139/ssrn.3734809","url":null,"abstract":"Text detection and recognition in natural scene images is a computer vision problem that remained a challenge for computer engineers for quite a long time. The new advancements in deep learning have revolutionized the world of computer vision. This paper attempts to build a Deep Learning (DL) based Text detection and recognition model for interpreting the text in natural scene images. The proposed model consists of three stages namely candidate text region detection, text region extraction, and text recognition. The natural scene image is first fed to the candidate text region detection mechanism which extracts potential regions containing text characters. The regions containing non-text which are introduced in the first stage of processing are filtered in the second stage. The set of text regions resulted from the second stage is then recognized in the final stage. Maximally Stable Extremal Region (MSER) algorithm is used in the candidate text region detection. Two convolutional neural networks, one in the text region extraction stage and the other one in the text recognition stage, are used in the proposed model. Text detection in natural scenes is not an easy problem as it appears. The complexity of detection and recognition of text characters in natural scene images is mainly due to the diversity of the textual characters and the natural scene, presence of various disturbances, different illumination conditions, different color, size, and area of the text. ICDAR-2011, ICDAR-2013, CHARS-74K, and CIFAR-100 datasets are used for training and validating our models.","PeriodicalId":145147,"journal":{"name":"CompSciRN: Audio","volume":"16 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113968347","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}

引用次数: 0

Movie Recommendation System Using Item Based Collaborative Filtering 基于项目协同过滤的电影推荐系统

CompSciRN: Audio Pub Date : 2020-07-01 DOI: 10.21276/ijircst.2020.8.4.2

Poonam Sharma, L. Yadav

引用次数: 6