24,909 research outputs found

    Digital archives: essential elements in the workflow for endangered languages documentation

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    Manuscript transcription by crowdsourcing: Transcribe Bentham

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    Transcribe Bentham is testing the feasibility of outsourcing the work of manuscript transcription to members of the public. UCL Library Services holds 60,000 folios of manuscripts of the philosopher and jurist Jeremy Bentham (1748-1832). Transcribe Bentham will digitise 12,500 Bentham folios, and, through a wiki-based interface, allow volunteer transcribers to take temporary ownership of manuscript images and to create TEI-encoded transcription text for final approval by UCL experts. Approved transcripts will be stored and preserved, with the manuscript images, in UCL's public Digital Collections repository. The project makes innovative use of traditional Library material. It will stimulate public engagement with UCL's scholarly archive collections and the challenges of palaeography and manuscript transcription; it will raise the profile of the work and thought of Jeremy Bentham; and it will create new digital resources for future use by professional researchers. Towards the end of the project, the transcription tool will be made available to other projects and services

    Information hiding through variance of the parametric orientation underlying a B-rep face

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    Watermarking technologies have been proposed for many different,types of digital media. However, to this date, no viable watermarking techniques have yet emerged for the high value B-rep (i.e. Boundary Representation) models used in 3D mechanical CAD systems. In this paper, the authors propose a new approach (PO-Watermarking) that subtly changes a model's geometric representation to incorporate a 'transparent' signature. This scheme enables software applications to create fragile, or robust watermarks without changing the size of the file, or shape of the CAD model. Also discussed is the amount of information the proposed method could transparently embed into a B-rep model. The results presented demonstrate the embedding and retrieval of text strings and investigate the robustness of the approach after a variety of transformation and modifications have been carried out on the data
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