PPT-Optical Character Recognition

QuratulAin Ainie Akram Sarmad Hussain Center for language Engineering Al Khawarizmi Institute of Computer Science University of Engineering and Technology Lahore Pakistan Lecture . The process of OCR involves several steps including segmentation feature extraction and classification Each of these steps is a field unto itself and is described briefly here in the context of a Matlab implementation of OCR One example of OCR is sh However unlike electrical current that 64258ows through a metal strip according to Ohms law optical waves travel in the waveguide in distinct opti cal modes A mode in this sense is a spatial distribution of optical energy in one or more dimensions t Rights Reserved Page | 85 Volume 2, Issue 5 , May 2012 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available Alternately Trained Relaxation Convolutional Neural Network Chunpeng Wu, Wei Fan, Yuan He, Jun Sun, Satoshi Naoi Fujitsu R&D Center, Co ., Ltd . Sep 1st, 2014 Copyright 2014 FUJITSU R&D CENTER CO., Eric Brenner. Paul Carpenter. Daniel Ehrenberg. Aaron McCarty. Travis Raines. Advised by Jeff . Ondich. Defining the Problem. What is optical character recognition (OCR)?. Input: an image of some text. Using the Ullman Algorithm for Graphical Matching. Iddo. . Aviram. OCR- a Brief Review. Optical character recognition. , usually abbreviated to . OCR. , is the mechanical or electronic translation of scanned images of handwritten, typewritten or printed text into machine-encoded text. AP English . IV. Review text in the Bedford, 1506-1508. What is tragedy? . A literary tragedy presents courageous individuals who confront powerful forces within or outside themselves with a dignity that reveals the breadth and depth of the human spirit in the face of failure, defeat, and even death (. 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. . polarised. emission in X-ray binaries. Dave Russell. New York University Abu Dhabi. dave.russell@nyu.edu. In collaboration with T. ariq . Shahbaz. (IAC, Tenerife). I’d also like to thank Rob Fender, Elena Gallo, . By: . Mutee. U . Rahman. Muhammad Rafi. Waleed. Butt. سنڌي عڪسي اکرن جي سڃاڻپ. Summary of the Project . Total 15 main bodies are considered. Due to complications diacritics are not considered. using Hidden Markov Models. Jan . Rupnik. Outline. HMMs. Model parameters. Left-Right. models. Problems. OCR - Idea. Symbolic example. Training. Prediction. Experiments. HMM. Discrete Markov model : probabilistic finite state machine. About Your Presenter. Presenting today:. Juan Worle. Technical Training . Coordinator. Microscan Corporate Headquarters Renton, WA. Course . Objectives. By completing this webinar you will:. Understand definition of OCR . 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. NAME: . PAVITHRA J. DESIGNATION: . ASSISTANT PROFESSOR. SUBJECT CODE: . 18PHY12. OPTICAL FIBERS. CONTENTS. Introduction. Principle. Types of Optical Fibers. Applications. Conclusions. Fiber-optic communication is a method of transmitting information from one place to another by sending pulses of light through an optical fiber..