PPT-Optical Character Recognition:

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 machineencoded text. 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 Nicholas A. Kuhta. Oregon State University – Physics. kuhtan@onid.orst.edu. Oregon State University SSO Seminar 04/06/2010. 1. Collaborators. : . Bill Cowell. (OSU Electrical Engineering). Chris Knutson . Tybalt. is angry and hot-blooded. His foil is . Benvolio. who is thoughtful and peaceful. . “TYBALT: What, art thou drawn among these heartless hinds?... BENVOLIO: I do but keep the peace” (I, . Technical Advances Towards Terabit Networking. Geoff Bennett. Director, Solutions & Technology. C. oined by Andrew Schmitt from . Infonetics. Describes the . transformation. (as opposed to evolution) of optical transport networks in response to an exponential growth in demand. Get the Facts. This presentation was part of a Pre-Conference seminar at the 2014 BICSI Winter Conference. The slides shown here were presented by. Loni Le Van-. Etter. , 3M. What Is A Passive Optical LAN ?. . Presented by. ,. . . PRAVIN. M. R. 7. 45. . This is what it really does…. 2. - Optical Camouflage -. What is . Optical. Camouflage. ??. It is developed by three scientists at the university of Tokyo.. What do you see first?. An old witch or a young woman?. A musician or . a girl’s face. ?. Duck or Rabbit?. Young Girl or Old Woman?. Father or son?. Swan or Squirrel?. Seal or Donkey?. Duck or Rabbit?. 14. CO. 2. ) at and below ambient levels. D. A. Long, . A. J. Fleisher, Q. Liu, and J. T. Hodges. National Institute of Standards and Technology. Gaithersburg, Maryland. HDR Architecture, Inc.. Long-lived isotope of carbon (half-life 5,730 years). Linda Shapiro. CSE 455. 1. Face recognition: once you’ve detected and cropped a face, try to recognize it. Detection. Recognition. “Sally”. 2. Face recognition: overview. Typical scenario: few examples per face, identify or verify test example. What are optical illusions?. An experience of seeming to see something that does not exist or that is other than it appears. An experience of seeming to see something that does not exist or that is other than it appears. In this optical illusion, if you follow the movement of the rotating pink dot with your eyes, the dots will remain only one . colour. , pink. . However, if you stare at the black in the center, the moving dot will turn green. . 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. 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. 2. Question to Consider. What are the key challenges police officers face when dealing with persons in behavioral crisis?. 3. Recognizing a. Person in Crisis. Crisis Recognition. 4. Behavioral Crisis: A Definition.