A real-time character recognition system was developed to recognize upper case handprinted characters in a real-time small machine environment. The recognition system consists of two major components: namely, a data acquisition system and a pattern recognition system. The data acquisition system was designed and implemented to allow the real world data flow into the computer from a COMPUTER writing tablet in real time. The pattern recognition system was also designed and implemented to yield a decision on the input character in real time (user time). A curve optimization technique originally devised by Reumann and Witkam was modified to extract only the significant data that describes a character. Computations were minimized through mathematical simplifications, hardware-software trade-off, and special programming techniques at the machine level. In addition, the preprocessor operated concurrently with the data acquisition routine to reduce data storage requirements as well as to-provide fast response to handprinted inputs. A non-uniform quantization plane was proposed and implemented to discriminate pen directions. Stroke patterns of a character were recognized using a syntactic approach. Finally, recognized stroke patterns within a character were classified as one of the known pattern classes by two classification methods: dictionary look-up and a modified nearest neighbor rule, both guided by special geometric measurements on some character pairs. Character patterns were defined in the dictionary such that no user training or personalized dictionary is required for future use. A test was conducted using the ACM proposed upper case handprinted character set and a recognition rate of 98.3% was obtained from over 2300 characters of sizes varying from 1/4 inch tall to 3/4 inch tall from 10 people. It Is observed that the approach taken in this thesis can also be applied to recognized handprinted patterns other than the standard one proposed by the ACM. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

This thesis describes the simulation of a character recognition system using rive filter designs based on probabilistic models of character patterns. Four of the designs yield linear filters. Of these, three are based on variations of a Gaussian model. The fourth is based on the assumption of independent binary-valued features. The latter design is shown to produce higher recognition rates than any of the others when tested on Munson's multi-author hand-printed characters. This filter design is also tested on two subsets of the Cornell machine-printed data base. The fifth filter design is a special case of a quadratic filter, based on a Gaussian model in which spatially stationary covariance statistics are assumed. This assumption results in a filter structure consisting of a linear operation on the pattern vector plus a linear operation on the autocorrelation vector of the pattern. This filter design is found to achieve lower performance than the best linear filter design when tested on Munson's characters, and nearly equal performance on the Cornell characters. However, there are indications that a filter of this structure could achieve higher performance for some choice of filter coefficients. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

The objectives of the research were to determine whether there is a preferred device in the home environment that the consumer would perceive to be easy to use, offer a sense of security and allow the seamless purchase of commodity items. The research involved interviews with leading managers in large corporations that influence the development of products used in the home environment as well as a survey to a controlled group of consumer electronic experts. The research findings show that although there is much hype about the future of certain technology devices for use in the home environment, the consumer is interested in an easy to use technology that offers seamless connectivity in a secure environment with trusted service providers. Lastly, the findings established that further in depth research is required on the impact of the new wireless networks and mobile connectivity. / Mrs. Nikki Kettles

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This thesis is a benchmark performed on three commercial Optical Character Recognition (OCR) engines. The purpose of this benchmark is to characterize the performance of the OCR engines with emphasis on the correlation of errors between each engine. The benchmarks are performed for the evaluation of the effect of a multi-OCR system employing a voting scheme to increase overall recognition accuracy. This is desirable since currently OCR systems are still unable to recognize characters with 100% accuracy. The existing error rates of OCR engines pose a major problem for applications where a single error can possibly effect significant outcomes, such as in legal applications. The results obtained from this benchmark are the primary determining factor in the decision of implementing a voting scheme. The experiment performed displayed a very high accuracy rate for each of these commercial OCR engines. The average accuracy rate found for each engine was near 99.5% based on a less than 6,000 word document. While these error rates are very low, the goal is 100% accuracy in legal applications. Based on the work in this thesis, it has been determined that a simple voting scheme will help to improve the accuracy rate.

Thesis (M.S.)--Missouri University of Science and Technology, 2010. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed Dec. 23, 2009). Includes bibliographical references (p. 82-83).

published_or_final_version / Computer Science / Master / Master of Philosophy

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The basic goal of the Adaptive Optical Music Recognition system presented herein is to create an adaptive software for the recognition of musical notation. The focus of this research has been to create a robust framework upon which a practical optical music recognizer can be built. / The strength of this system is its ability to learn new music symbols and handwritten notations. It also continually improves its accuracy in recognizing these objects by adjusting internal parameters. Given the wide range of music notation styles, these are essential characteristics of a music recognizer. / The implementation of the adaptive system is based on exemplar-based incremental learning, analogous to the idea of "learning by example," that identifies unknown objects by their similarity to one or more of the known stored examples. The entire process is based on two simple, yet powerful algorithms: k-nearest neighbour classifier and genetic algorithm. Using these algorithms, the system is designed to increase its accuracy over time as more data are processed.