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A survey of non-thinning based vectorization methods
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<span title="">1998</span>
<i title="Springer Berlin Heidelberg">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a>
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We survey the methods developed up to date for crude vectorization of document images. We classify them into six categories: thinning based, Hough Transform based, contour-based, run-graph based, mesh-pattern based, and sparse pixel based. The crude vectorization is a relatively mature subject in the Document Analysis and Recognition field, though there are rooms to improve. The purpose of the survey is to provide researchers with a comprehensive overview of this technique for them to choose a
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... uitable method when developing their vectorization algorithms and systems.
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