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Our system adopts a region-based image segmentation framework. Multi-resolution super-pixels are first extracted to represent each image. A Support Vector Machine (SVM) classifier is used to label each super-pixel region with a probability to belong to foreground glyph strokes. Pixelwise probability maps from multiple super-pixel resolution scales are then aggregated to cope with various stroke widths and background noise. A fully connected Conditional Random Field model is then applied to improve the labeling consistency. Segmentation results show that our system preserves delicate local details of the historic Maya glyphs with various stroke widths and also reduces background noise. As an application, we conduct retrieval experiments using the extracted binary images. Experimental results show that our automatically extracted glyph strokes achieve comparable retrieval results to those obtained using glyphs manually segmented by epigraphers in our team.<\/jats:p>","DOI":"10.1145\/2996859","type":"journal-article","created":{"date-parts":[[2017,4,14]],"date-time":"2017-04-14T12:18:48Z","timestamp":1492172328000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Extracting Maya Glyphs from Degraded Ancient Documents via Image Segmentation"],"prefix":"10.1145","volume":"10","author":[{"given":"Rui","family":"Hu","sequence":"first","affiliation":[{"name":"Idiap Research Institute, Martigny, Switzerland"}]},{"given":"Jean-Marc","family":"Odobez","sequence":"additional","affiliation":[{"name":"Idiap Research Institute and \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL)"}]},{"given":"Daniel","family":"Gatica-Perez","sequence":"additional","affiliation":[{"name":"Idiap Research Institute and \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL)"}]}],"member":"320","published-online":{"date-parts":[[2017,4,12]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.120"},{"key":"e_1_2_1_2_1","first-page":"594","article-title":"Compound binarzation for degraded document images","volume":"10","author":"Khatatneh Arwa Mahmoud","year":"2015","unstructured":"Arwa Mahmoud AL- Khatatneh , Sakinah Ali Pitchay , and Musak Kasim Al-qudah. 2015 . 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