Titlebook: Document Analysis and Recognition - ICDAR 2024; 18th International C Elisa H. Barney Smith,Marcus Liwicki,Liangrui Peng Conference proceedi

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書目名稱Document Analysis and Recognition - ICDAR 2024被引頻次學科排名




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書目名稱Document Analysis and Recognition - ICDAR 2024讀者反饋學科排名

高射炮

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/e/image/284810.jpg

Inkling

allenges of this task is that documents generally exhibit degradations and acquisition artifacts that can greatly vary throughout the page. Nonetheless, even when dealing with a local patch of the document, taking into account the overall appearance of a wide portion of the page can ease the predict

Benign

ailability, and complex orthographic diacritics. The dataset consists of roughly 80,000 annotated text-line images from 1700 pages of . to . century documents, including a training set with text-line images from the . to . century and two test sets. One is distributed similarly to the training set w

Atrium

Arctic

ion about individuals living in France and their households. We aim at extracting all the information contained in these tables using automatic handwritten table recognition. At the end of the Socface project, in which our work is taking place, the extracted information will be redistributed to the

Arctic

MILK

bution, scribe-papermaker relation, trading, etc.) in Humanities such as Musicology. As of today, they come in a variety of formats: digitized handtracings and rubbings, X-ray based imagery and, more recently, thermograms acquired with infrared (IR) cameras –?all of which have been made accessible v

MEET

tudy the diffusion of astronomical knowledge on a global scale. However, state-of-the-art vectorization methods, often designed to tackle modern data, are not adapted to the complexity and diversity of historical astronomical diagrams. Our contribution is thus twofold. First, we introduce a unique d

hangdog

of text, covering more than half of the 19th century (i.e., 1817) until the late part of the 20th century (i.e., 1990). Eight native Romanian speakers annotated the dataset with five named entities. The samples belong to one of the following four historical regions of Romania, namely Bessarabia, Mo