This thesis describes the design and instrumentation of an opaque contour-tracing scanner for studies in optical character recognition (OCR). Most previous OCR machines have attempted to recognize characters by mask matching, a technique which requires a large and expensive computer, and which is sensitive to small changes in type font. Contour tracing is a promising new approach to OCR. In contour tracing, the outside of the character is followed, and the resulting horizontal and vertical co-ordinates, X(t) and Y(t), of the scanning spot are processed for recognition. Although much additional research is required on both scanner design and processing algorithms, it is expected that an OCR device which uses a contour-tracing scanner will be significantly less expensive than existing multifont recognition machines. In this thesis, four possible contour-tracing scanners are proposed and evaluated on the basis of cost, complexity and availability of components. The design that was chosen for construction used an X-Y oscilloscope and a photomultiplier as a flying-spot scanner. In instrumenting this design, a digital-to-analogue converter, an up-down counter and many other special purpose logic circuits were designed and constructed. The scanner successfully contour traced Letraset characters, typewritten characters and handprinted characters. At the machines maximum speed, a character is completely traced in approximately 10 msec. Photographs of contour traces and the X(t) and Y(t) waveforms are included in the thesis. Although the present system will only trace two adjacent characters, proposed modifications to the system would enable an entire line of characters to be contour-traced. Included in the thesis are recommendations for further research on scanner design. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

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We have examined the technique of persistent spectral holeburning as a method for analog optical data storage. Two types of materials were examined from a theoretical standpoint, ones which exhibit photochemical holeburning (PHB) and ones which exhibit nonphotochemical holeburning (NPHB). We have presented the conditions under which a PHB material shows a linear relation between hole depth and exposure intensity or exposure time. Also we show that a NPHB material has no such condition. We conclude that a PHB material may be useful for analog optical data storage, while a NPHB material would not. Experiments were conducted with a NPHB material, R' color centers in LiF, to support the NPHB analysis.

Photocopy of typescript. / Digitized by Kansas Correctional Industries

A major problem in robotics vision is the segmentation of images of natural scenes in order to understand their content. This thesis presents a new solution to the image segmentation problem that is based on the design of a rule-based expert system. General knowledge about low level properties of an image is formulated into production rules. A number of processes employ the rules to segment the image into uniform regions and connected lines. In addition to the knowledge rules, a set of control rules are also employed. These include meta-rules that embody inferences about the order in which the knowledge rules are matched. They also include focus of attention rules that determine the path of processing within the image. A third set of rules contains the strategy rules which are data-driven inferences about the control rules. They dynamically modify the processing strategy. Different rule ordering and focus of attention strategies are selected according to a set of performance parameters. These measure the quality of the segmentation output at any point in time. Experiments with the knowledge rules resulted in an optimal set based on output quality and processing efficiency. Overall system performance is shown to be qualitatively and quantitatively superior to previous segmentation algorithms.

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M. S.

M.S.

M.Com. (Informatics) / Please refer to full text to view abstract

D.Ing. (Electrical and Electronic ) / This thesis describes a neural network based system for the classification of handwritten digits as found on real-life mail pieces. The proposed neural network uses a modular architecture which lends itself to parallel implementation. This modular architecture is shown to produce adequate performance levels while significantly reducing the required training time. The aim of the system is not only to achieve a high recognition performance, but also to gain more insight into the functioning of the neural networks. This is achieved by using separate feature extraction and classification stages. The output of the feature extraction stage gives a good indication of the final performance level of the classifier, even before training. The need for an optimal feature set is expressed to elevate the performance levels even further.