PPT-Text Recognition and Retrieval in Natural Scene Images

Author : nonhurmer | Published Date : 2020-06-24

Udit Roy CVIT IIIT Hyderabad Advisor C V Jawahar Coadvisor Karteek Alahari Inria Overview Introduction Text Detection Cropped Word Recognition amp Retrieval EndtoEnd

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Text Recognition and Retrieval in Natural Scene Images: Transcript

Udit Roy CVIT IIIT Hyderabad Advisor C V Jawahar Coadvisor Karteek Alahari Inria Overview Introduction Text Detection Cropped Word Recognition amp Retrieval EndtoEnd Frameworks. Wu Andrew Y Ng Computer Science Department Stanford University 353 Serra Mall Stanford CA 94305 USA acoatesblakeccbcasessanjeevbipinstwangcatdwu4ang csstanfordedu Abstract Reading text from photographs is a challenging problem that has received a si T he images can be obtained using muliple cameras or one mo ving camera T he term binocular vision is used when tw oc ameras are emplo yed brPage 2br 2 Ster eo setup and terminology Fixation point the point of intersection of the optical axis Baseli Document Image Retrieval. David Kauchak. cs160. Fall 2009. adapted from. :. David . Doermann. http://terpconnect.umd.edu/~oard/teaching/796/spring04/slides/11/796s0411.ppt. Assign 4 . writeups. Overall, I was very happy. Ing. . Lukáš Neumann. Supervisor. : . Prof. Dr. Ing. Ji. ří. Matas. Problem Introduction. Input. : . Digital image . (BMP, JPG, PNG). / video (AVI). Output. : . Set of words in the image. . . Quattoni. Antonio . Torralba. . CSAIL, MIT CSAIL, MIT . UC Berkeley EECS & ICSI 32 Vassar St., Cambridge, MA 02139 . ariadna@csail.mit.edu. . torralba@csail.mit.edu. . Recognizing Indoor Scenes. Andrew Chi. Brian Cristante. COMP 790-133: January 27, 2015. Image Retrieval. AI / Vision Problem. Systems Design / Software Engineering Problem. Sensory Gap. : “What features should we use?”. Query-Dependent?. Nikhil . Rasiwasia. , . Nuno. . Vasconcelos. Statistical Visual Computing Laboratory. University of California, San Diego. Thesis Defense. Ill pause for a few moments so that you all can finish reading this. . Information Retrieval. Information Retrieval. Konsep. . dasar. . dari. IR . adalah. . pengukuran. . kesamaan. sebuah. . perbandingan. . antara. . dua. . dokumen. , . mengukur. . sebearapa. . Badruz. . Nasrin. Bin Basri. 1051101534. . Supervisor : . Mohd. . Haris. Lye Abdullah. 1. Contents. Introduction. 1. Literature review . 2. Method . Used. . 3. Experiment and Result. 4. Future works. Classification, Annotation and Segmentation in an . Automatic . Framework. Li-. Jia. Li, Richard . Socher. , Li . Fei-Fei. 1. 2. City Travel. Pagoda. Sunrise. Sunshine. Sun. 3. City Travel. Pagoda. Sunrise. Hongning. Wang. CS@UVa. CS@UVa. CS6501: Information Retrieval. 1. Abstraction of search engine architecture. User. Ranker. Indexer. Doc Analyzer. Index. results. Crawler. Doc . Representation . Query Rep. Visual Persuasion in a Litigation Trial: The Case of Real Photographic Images vs. Sketched Images This study was designed to determine which kinds of graphic images were most useful as evidence in trials. Rosalia F. Tungaraza. Advisor: Prof. Linda G. Shapiro. Ph.D. Defense. Computer Science & Engineering. University of Washington. 1. Functional Brain Imaging. Study how the brain works . Imaging while subject performs a task . Linda Shapiro. ECE P 596. 1. What’s Coming. Review of . Bakic. flesh . d. etector. Fleck and Forsyth flesh . d. etector. Review of Rowley face . d. etector. Overview of. . Viola Jones face detector with .

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