Marginal Noise Reduction in Historical Handwritten Documents: A Survey

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Chakraborty, Arpita
Blumenstein, Michael
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Lisa OConner

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2016
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GREECE

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This paper presents a survey on different approaches for removing the marginal noise from document images, and anlaysing the research challenges of those methods relating to handwritten historical datasets. In this survey, historical documents collected from Australian Archives and Libraries are introduced and the associated layout complexities of those document images are also described. Benchmarking other historical databases related to this work is also discussed. This survey discusses the difficulties and suitability of the state-of-the-art methods to remove marginal noise as well as preserving the text content from handwritten historical documents. This survey helps researchers to identify appropriate methods according to the associated marginal noise and also illustrates their drawbacks in order to make suggestions for developing approaches, which are more general and robust for any datasets.

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PROCEEDINGS OF 12TH IAPR WORKSHOP ON DOCUMENT ANALYSIS SYSTEMS, (DAS 2016)

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Artificial intelligence not elsewhere classified

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