Shape-free statistical information in optical character recognition.
Published: 2007
Language: eng
Number of pages: 129
ISBN-13: 9780494272909
Description: The fundamental task facing Optical Character Recognition (OCR) systems involves the conversion of input document images into corresponding sequences of symbolic character codes. Traditionally, this has been accomplished in a bottom-up fashion: the image of each symbol is isolated, then classified based on its pixel intensities. While such shape-based classifiers are initially trained on a wide array of fonts, they still tend to perform poorly when faced with novel glyph shapes. In this thesis, we attempt to bypass this problem by pursuing a top-down "codebreaking" approach. We assume no a priori knowledge of character shape, instead relying on statistical information and language constraints to determine an appropriate character mapping. We introduce and contrast three new top-down approaches, and present experimental results on several real and synthetic datasets. Given sufficient amounts of data, our font and shape independent approaches are shown to perform about as well as shape-based classifiers.
Notes: Source: Masters Abstracts International, Volume: 45-06, page: 3189.

Advisor: S. Roweis.

Thesis (M.Sc.)--University of Toronto, 2007.

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