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M.Com. (Informatics) / This dissertation examines the field of computer vision, with special attention given to the recognition of alpha numeric characters on video images using OCR software. The study may be broadly divided into four sections. The first section offers an introduction to standard OCR (Optical Character Recognition) methods that have evolved over the years and have been incorporated into some commercial software packages currently. The second section covers the problem of reading characters in a dynamic environment and also the problems experienced with the compatibility of current OCR software products. The third section of the dissertation looks at solutions for the problem mentioned in section two and creates a framework for a generic model in which any application should fit. The generic model is then described in detail. The framework should provide a foundation for interested parties to build, modify or improve the model. The final section gives examples of how the model should present a solution. Experimental results are looked at and the model is critically evaluated.

Report (M.S.)--Virginia Polytechnic Institute and State University. M.S. 1993. / Abstract. Includes bibliographical references (leaf 50). Also available via the Internet.

Thesis (M.Eng.Sc.) - University of Queensland, 2004. / Includes bibliography.

This study proposes a generic model for optical character recognition (OCR) on remote objects, using digitised video and digital camera input. Currently, most applications using this technique are ad hoc. Lack of formalism creates the risk of fragmented adhocracy. The purpose of this model is to introduce some degree of formalism based on classical character recognition, thereby providing a theoretical basis, and to explore the potential of character recognition on digital images for automated reading. The model also propogates the use of off the shelf commercial software and hardware currently available. The model “CRODI” which is an abbreviation for Character Recognition On Digital Images, consists of several objects or building blocks necessary for any remote character recognition application. In order to provide an optimal solution for any application it is necessary to select the most appropriate and effective building blocks. The combination and sequence in which these building blocks are selected is called a visual routine. This study does not only focus on choosing the correct building blocks for an application but also describes how certain visual routines and methodologies can be used to cater for applications with a certain profile. Furthermore, a formal notation in the form of an Expert Support System is introduced to describe optimised visual routines and the selection of building blocks in an efficient way. An example is given in chapter six of the efficiency and effectiveness of the model on a classical but dynamic application, where character recognition is performed on stationary and fast moving vehicles’ number plates. The biggest advantage of CRODI is to propose a generic framework or model from which an intuitive selection of objects, visual routines or methodologies can occur to expedite and optimise remote character recognition on almost any stationary or moving object. / Ehlers, E.M., Prof.

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