The New Zealand Electronic Text Centre collections provide open access to significant New Zealand and Pacific Island texts and materials.
This encompasses both digitised heritage material and born-digital resources. The collections contain over 2,600 texts (around 65,000 pages) which are made available in several formats and, where possible, under a Creative Commons license.
The overall price of your project is determined using our price matrix. This involves three characteristics: typeface, legibility, and condition. A text that uses a standard modern or equivalent typeface is easier to digitize than a text that uses an obscure or difficult to decipher typeface or handwriting. Likewise, a text that is clear and uses a minimal number of character sets, or a text on pages that are not marred by physical damage such as smudges, tears, or unusual textual features, will be easier to digitize than a smudged text on worn pages. Learn more about the types of documents that can be submitted.
A hybrid of window system, shell, and editor, Acme gives text-oriented applications a clean, expressive, and consistent style of interaction. Traditional window systems support interactive client programs and offer libraries of pre-defined operations such as pop-up menus and buttons to promote a consistent user interface among the clients. Acme instead provides its clients with a fixed user interface and simple conventions to encourage its uniform use.
ALTO (Analyzed Layout and Text Object) is a XML Schema that details technical metadata for describing the layout and content of physical text resources, such as pages of a book or a newspaper. It most commonly serves as an extension schema used within the Metadata Encoding and Transmission Schema (METS) administrative metadata section. However, ALTO instances can also exist as a standalone document used independently of METS.
Y. Yang, и J. Pedersen. Proceedings of the Fourteenth International Conference on Machine Learning, стр. 412--420. San Francisco, CA, USA, Morgan Kaufmann Publishers Inc., (1997)
D. Shen, J. Sun, Q. Yang, и Z. Chen. WWW '06: Proceedings of the 15th international conference on World Wide Web, стр. 643--650. New York, NY, USA, ACM Press, (2006)
C. Zhai, A. Velivelli, и B. Yu. Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, стр. 743--748. New York, NY, USA, ACM, (2004)
P. Kluegl, M. Atzmueller, и F. Puppe. Proc. LWA 2009, Knowledge Discovery and Machine Learning Track, Darmstadt, Germany, University of Darmstadt, (2009)