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发表于 2025-3-21 19:17:02 | 显示全部楼层 |阅读模式
书目名称Graphics Recognition. Recent Advances
编辑Atul K. Chhabra,Dov Dori
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: ;
出版日期Conference proceedings 2000
版次1
doihttps://doi.org/10.1007/3-540-40953-X
isbn_softcover978-3-540-41222-9
isbn_ebook978-3-540-40953-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
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https://doi.org/10.1007/b137223hics recognition. Our system uses an explicit descriptive model of the document class to find the most likely interpretation of a scanned document image. In contrast to the traditional pipeline architecture, our system carries out all stages of the analysis with a single inference engine, allowing for an end-to-end propagation of the uncertainty.
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Sebastian F. Schoppmann,Martin Rieglerrning, or synthesis, of representative graphical symbols from given examples. Graphical symbols are represented by graphs. This way the learning task is transformed into that of computing the generalized median of a given set of graphs, which is a novel graph matching problem and solved by a genetic algorithm.
发表于 2025-3-22 18:06:12 | 显示全部楼层
Stable and Robust Vectorization: How to Make the Right Choicesbe taken into account when choosing the different steps of one’s vectorization method. We stress the importance of having robust methods and stable implementations, and we base ourselves extensively on our own implementations and tests, concentrating on methods designed to have few, if any, parameters.
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Symbol and Shape Recognition last ten years, in the different application fields, are reviewed. Methods used in the representation, description and classification phases are shortly discussed and the main recognition strategies are mentioned. Some of the problems that appear still open are proposed to the attention of the reader.
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