A survey of non-thinning based vectorization methods [chapter]

Liu Wenyin, Dov Dori
<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> </i> &nbsp;
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
more &raquo; ... uitable method when developing their vectorization algorithms and systems.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/bfb0033241">doi:10.1007/bfb0033241</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/76arsrgscjc3njopgmiqr3t4dm">fatcat:76arsrgscjc3njopgmiqr3t4dm</a> </span>
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