PPT-Feature Extraction for Java Character Recognition

Rudy Adipranata Liliana Meiliana Indrawijaya Gregorius Satia Budhi Informatics Department Petra Christian University Siwalankerto 121131 Surabaya Indonesia Agenda. 63 Menu Tracking and Natural Language Commands All FEATURE Description Language Legal Professional Premium Home Dictate for Mac Application Support Word Processing Word 2003 2007 and 2010 WordPad XP Vista Windows 7 and DragonPad word processor in 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 . Course Introduction. Typical . Applications. Resources:. . Syllabus. Internet Books and Notes. D.H.S: Chapter 1. Glossary. LECTURE 01: . COURSE OVERVIEW. Pattern Recognition: . “the act of taking raw data and taking an action based on the category of the pattern.”. R. K. Sharma. Thapar university, . patiala. . Handwriting Recognition System. The . technique by which a computer system can recognize characters and other symbols written by hand in natural handwriting is called handwriting recognition (HWR) system. . 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. Piet Martens (Physics) & . Rafal. . Angryk. (CS). Montana State University. A Computer Science Approach to Image Recognition. Conundrum. : We can teach an undergraduate in ten minutes what a filament, sunspot, sigmoid, or bright point looks like, and have them build a catalog from a data series. Yet, teaching a computer the same is a very time consuming job – plus it remains just as demanding for every new feature.. electroencephalographic records . using . EEGFrame . framework. Alan Jović, Lea Suć, Nikola Bogunović. Faculty of Electrical Engineering and Computing, University of Zagreb. Department of Electronics, Microelectronics, Computer and Intelligent Systems. Microsoft Corporate. ganghua@microsoft.com. Online Contextual Face Recognition: . Towards Large Scale Photo Tagging for Sharing. Photo sharing has become a main online social activity. FaceBook. receives 850 million photo uploads/month. Principle Component Analysis. Why Dimensionality Reduction?. It becomes more difficult to extract meaningful conclusions from a data set as data dimensionality increases--------D. L. . Donoho. Curse of dimensionality. 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. Gaussian Distribution. variance. Standard deviation. Statistical representation . and . independence. of random variables. Probability density can be not Gaussian. Variables can be dependent. problems. Introducing the tasks:. Getting simple structured information out of text. Information Extraction. Information extraction . (IE) systems. Find and understand limited relevant parts of . texts. Gather information from many pieces of text. Definition. The Comic Hero. Displays at least the minimal level of personal charm or worth of character it takes to win the audience’s basic approval and support. “Average to below average” in terms of moral character. Definition. The Comic Hero. Displays at least the minimal level of personal charm or worth of character it takes to win the audience’s basic approval and support. “Average to below average” in terms of moral character.