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Writer recognition by characters, words and sentences

  • The methods developed in the research project "Herbar Digital" are to help plant taxonomists to master the great amount of material of about 3.5 million dried plants on paper sheets belonging to the Botanic Museum Berlin in Germany. Frequently the collector of the plant is unknown. So a procedure had to be developed in order to determine the writer of the handwriting on the sheet. In the present work the static character is transformed into a dynamic form. This is done with the model of an inert ball which is rolled through the written character. During this off-line writer recognition, different mathematical procedures are used such as the reproduction of the write line of individual characters by Legendre polynomials. When only one character is used, a recognition rate of about 40% is obtained. By combining multiple characters, the recognition rate rises considerably and reaches 98.7% with 13 characters and 93 writers (chosen randomly from the international IAM-database [3]). Another approach tries to identify the writer by handwritten words. The word is cut out and transformed into a 6-dimensional time series and compared e.g. by means of DTW-methods. A global statistical approach using the whole handwritten sentences results in a similar recognition rate of more than 98%. By combining the methods, a recognition rate of 99.5% is achieved.

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Author:Martin Gehrke, Karl-Heinz SteinkeGND, Robert Dzido
Document Type:Conference Proceeding
Year of Completion:2009
Publishing Institution:Hochschule Hannover
Release Date:2009/11/24
Tag:Erkennungssoftware; OCR
Herbar Digital; Recognition software
GND Keyword:Herbarium; Angewandte Botanik; Gepresste Pflanzen; Digitalisierung; Virtualisierung
Source:IEEE International Carnahan Conference 2009, Zürich October 2009
Link to catalogue:613575601
Institutes:Fakultät I - Elektro- und Informationstechnik
DDC classes:004 Informatik
Licence (German):License LogoCreative Commons - Namensnennung-Nicht kommerziell-Keine Bearbeitung 3.0