@conference {255,
	title = {Improvement of Omnipage18{\textquoteright}s Efficiency},
	booktitle = {IEEE International Conference on Computer Science and Automation Engineering (CSAE 2012)},
	year = {2012},
	month = {05/2012},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Zhangjiajie},
	abstract = {In many Botanical Museums in the world exist millions of dried plants on paper sheets. They were collected in the last two hundred years. The collectors left annotations about the plants on the sheets. Some of them are written in handwriting others by a typewriter. There exist also comments on printed forms which are glued on the sheets. By an automatic OCR of the scanned herbarium objects the recognized texts can be put into a database. Commercial OCR-programs like Finereader or Omnipage are capable to recognize undisturbed printed texts in a correct way. Unfortunately we deal with historical material which often contains overwritten or crossed out words, the paper is yellowed, molded and has other artifacts. Moreover it seems to be extremely complicated to detect text regions in a complex environment with objects like roots, leaves, stamps, bar codes, yardsticks, color charts etc.. The old writing leads to read errors which are not the biggest problem, because they can be compensated by tolerant database queries. Worse is that sometimes text regions cannot be localized. In this paper a method is presented which helps Omnipage to detect missed text and also can distinguish printed text from handwriting.},
	keywords = {handwriting, localisation, Omnipage18, text region},
	author = {KH Steinke}
}
@conference {253,
	title = {Label Recognition in Herbarium Specimens by using Database-Queries},
	booktitle = {ICDM 2011 IEEE International Conference on Data Mining},
	year = {2011},
	month = {09/2011},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Vancouver, Canada},
	abstract = {For hundreds of years plant specimens are collected in herbariums for scientific purposes. These plants are mounted on specimen sheets and labeled with the essential data such as the name of the collector, the date and the place it was found. To make them available for a wider public the specimen sheets get digitized for online use. The purpose of our project in cooperation with the Botanical Garden in Berlin (Germany) is the development of a software-system for the analysis of these high resolution images. There are different approaches like template matching or SURF for detecting stable objects like color-charts, rulers, barcodes or even labels (fig. 1) on specimens. This paper describes a new method for the automatic scale- and rotation-invariant recognition of prior defined label-types by using OCR-engine generated texts contained in a database.},
	keywords = {database, label recognition, OCR-texts, rotation-invariant, scale-invariant, SURF, template matching},
	author = {M Gehrke and KH Steinke and R Dzido}
}
@conference {254,
	title = {Object Recognition in Herbarium Specimens},
	booktitle = {ICDM 2011 IEEE International Conference on Data Mining},
	year = {2011},
	month = {09/2011},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Vancouver, Canada},
	abstract = {About 3.5 million dried plants on paper sheets are deposited in the Botanical Museum Berlin in Germany. The aim of our project is the image analysis of scanned high resolution images. There are many different objects contained in a herbarium sheet like ruler, barcode, stamps, color-chart, labels with printed title, red type designator, envelopes, hand-written and printed annotations and the plant. Some objects are very stable like the ruler, others are extremely variable like handwriting or plant. So each class of objects requires a special detection method. Algorithms like template matching or SURF can be applied to some of the stable objects. For variable objects like handwriting detection methods are already developed by the authors and are described in other papers.},
	keywords = {best template, connected component analysis, hue space, object recognition, SURF, template matching},
	author = {KH Steinke and M Gehrke and R Dzido}
}
@conference {244,
	title = {Recognition of Humboldt{\textquoteright}s Handwriting in Complex Surroundings},
	booktitle = {12th International Conference on Frontiers in Handwriting Recognition (ICFHR)},
	year = {2010},
	month = {11/2010},
	address = {Kolkata},
	abstract = {In the Botanic Museum of Berlin in Germany exist about 3.5 million dried plants on paper sheets. They were collected in the last two hundred years by many collectors among those was the  famous researcher {\textquotedblleft}Alexander von Humboldt{\textquotedblright}. because e every collector left his handwriting on the sheet the question came up if it is possible to find out automatically which sheet belongs to  Humboldt{\textquoteright}s collection. To solve the problem many challenging sub problems have to be solved before. First text regions have to be localized and extracted. Then printing is to be distinguished from handwriting. For the latter an off-line writer recognition procedure had to be developed in order to determine the writer of the handwriting. Often two or more writers can be found on one sheet. To make sure that the suspect writer is correct an interactive method is applied which transforms the static character into a dynamic form. Different mathematical procedures are used such as the reproduction of the write line of individual characters by Legendre polynomials. All methods were proved on the international IAMdatabase [3]. 93 writers with at least 5 samples were chosen  from the IAM-database. By combining multiple characters (up to thirteen), the recognition rate rises considerably and reaches 98.7\%. A global statistical approach using the whole handwritten text results in a similar recognition rate of more than 98\%. By combining the methods by a ranking method a recognition rate of 99.5\% is achieved.},
	keywords = {Legendre polynomials, text localization, writer recognition},
	doi = {10.1109/ICFHR.2010.91},
	url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=5693621},
	author = {KH Steinke},
	editor = {M Gehrke}
}
