A PDF Test-Set for Well-Formedness Validation in JHOVE - the Good, the Bad and the Ugly
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The Application of File Identification, Validation, and Characterization Tools in Digital Curation
THE APPLICATION OF FILE IDENTIFICATION, VALIDATION, AND CHARACTERIZATION TOOLS IN DIGITAL CURATION BY KEVIN MICHAEL FORD THESIS Submitted in partial fulfillment of the requirements for the degree of Master of Science in Library and Information Science in the Graduate College of the University of Illinois at Urbana-Champaign, 2011 Urbana, Illinois Advisers: Research Assistant Professor Melissa Cragin Assistant Professor Jerome McDonough ABSTRACT File format identification, characterization, and validation are considered essential processes for digital preservation and, by extension, long-term data curation. These actions are performed on data objects by humans or computers, in an attempt to identify the type of a given file, derive characterizing information that is specific to the file, and validate that the given file conforms to its type specification. The present research reviews the literature surrounding these digital preservation activities, including their theoretical basis and the publications that accompanied the formal release of tools and services designed in response to their theoretical foundation. It also reports the results from extensive tests designed to evaluate the coverage of some of the software tools developed to perform file format identification, characterization, and validation actions. Tests of these tools demonstrate that more work is needed – particularly in terms of scalable solutions – to address the expanse of digital data to be preserved and curated. The breadth of file types these tools are anticipated to handle is so great as to call into question whether a scalable solution is feasible, and, more broadly, whether such efforts will offer a meaningful return on investment. Also, these tools, which serve to provide a type of baseline reading of a file in a repository, can be easily tricked. -
Support for Digital Formats
Chapter 4 Support for Digital Formats ong-term renderability cannot be ensured without into significant properties is focused on formats. The detailed knowledge about and documentation of dig- InSPECT project of the U.K. Arts and Humanities Data Lital file formats. In this respect, digital formats are at Service is investigating the significant properties of raster the heart of digital preservation activities. images, structured text, digital audio, and e-mail messages, and new awards were recently granted to study e-learning objects, software, vector images, and moving images.1 Significant Properties The term significant properties is used to refer to the Readings Library Technology Reports Library Technology properties of digital objects that must be preserved over • Andrew Wilson, “Significant Properties Report,” Oct. time through preservation treatments such as migrations 2007, www.significantproperties.org.uk/documents/ or emulations in order to ensure the continued usability wp22_significant_properties.pdf. A cogent review of and meaning of the objects. (Significant characteristics, work to date undertaken for the InSPECT project. essential characteristics, and essence are less commonly • Margaret Hedstrom and Christopher Lee, “Signifi- used synonyms). The definition and determination of cant Properties of Digital Objects: Definitions, these properties constitute a critical and mostly unsolved Applications, Implications,” in Proceedings of issue in the field of digital preservation. the DLM-Forum 2002, http://ec.europa.eu/ Significant properties are usually categorized as per- transparency/archival_policy/dlm_forum/ taining to content, context, appearance, structure, and doc/dlm-proceed2002.pdf. Describes preliminary www.techsource.ala.org www.techsource.ala.org behavior. If, for example, the digital object in question research taking a rather broad view of significant were a chapter of a book in PDF format, the content might properties, although follow-up appears to be be the text and pictures, the context would be the biblio- unavailable. -
Ocrmypdf Documentation Release 9.0.0
ocrmypdf Documentation Release 9.0.0 James R. Barlow 2019-08-09 Contents 1 Introduction 3 2 Release notes 7 3 Installing OCRmyPDF 33 4 Installing additional language packs 43 5 Installing the JBIG2 encoder 45 6 Cookbook 47 7 OCRmyPDF Docker image 53 8 Advanced features 57 9 Using the OCRmyPDF API 63 10 Batch processing 67 11 PDF security issues 73 12 Common error messages 77 13 Indices and tables 79 Index 81 i ii ocrmypdf Documentation, Release 9.0.0 OCRmyPDF adds an OCR text layer to scanned PDF files, allowing them to be searched. PDF is the best format for storing and exchanging scanned documents. Unfortunately, PDFs can be difficult to modify. OCRmyPDF makes it easy to apply image processing and OCR to existing PDFs. Contents 1 ocrmypdf Documentation, Release 9.0.0 2 Contents CHAPTER 1 Introduction OCRmyPDF is a Python 3 package that adds OCR layers to PDFs. 1.1 About OCR Optical character recognition is technology that converts images of typed or handwritten text, such as in a scanned document, to computer text that can be searched and copied. OCRmyPDF uses Tesseract, the best available open source OCR engine, to perform OCR. 1.2 About PDFs PDFs are page description files that attempts to preserve a layout exactly. They contain vector graphics that can contain raster objects such as scanned images. Because PDFs can contain multiple pages (unlike many image formats) and can contain fonts and text, it is a good formats for exchanging scanned documents. A PDF page might contain multiple images, even if it only appears to have one image. -
Raster Still Images for Digitization: a Comparison of File Formats
Federal Agencies Digitization Guidelines Initiative Raster Still Images for Digitization A Comparison of File Formats Part 2. Detailed Matrix (multi-page) This document presents the information on multiple, easily printable pages. Part 1 provides the same information in a unified table to facilitate comparisons. April 17, 2014 The FADGI Still Image Working Group http://www.digitizationguidelines.gov/still-image/ Raster Still Images for Digitization: A Comparison of File Formats This is the template for the pages that follow. The reference is the detailed matrix in part 1 of this document set. ATTRIBUTE: Scoring conventions: Questions to Consider: TIFF Common TIFF, Uncompressed Common TIFF, Lossless Compressed GeoTIFF/BigTIFF, Uncompressed GeoTIFF/BigTIFF, Compressed JPEG 2000 JPEG 2000: JP2 JPEG 2000: JPX JPEG JPEG (JFIF with EXIF) PNG PNG PDF PDF (1.1-1.7) PDF/A (1, 1a, 1b, 2) GeoPDF* * GeoPDF refers to either TerraGo GeoPDF or Adobe Geospatial PDF 2 ATTRIBUTE: Sustainability Factors: Disclosure Scoring Conventions: Good, Acceptable, Poor Questions to Consider: Does complete technical documentation exist for this format? Is the format a standard (e.g., ISO)? Are source code for associated rendering software, validation tools, and software development kits widely available for this format? TIFF Common TIFF, Good Uncompressed Common TIFF, Good Lossless Compressed GeoTIFF/BigTIFF, Good Uncompressed GeoTIFF/BigTIFF, Good Compressed JPEG 2000 JPEG 2000: JP2 Good JPEG 2000: JPX Good JPEG JPEG (JFIF with EXIF) Good PNG PNG Good PDF PDF -
Policy-Making for Research Data in Repositories: a Guide
Policy-making for Research Data in Repositories: A Guide http://www.disc-uk.org/docs/guide.pdf Image © JupiterImages Corporation 2007 May 2009 Version 1.2 by Ann Green Stuart Macdonald Robin Rice DISC-UK DataShare project Data Information Specialists Committee -UK Table of Contents i. Introduction 3 ii. Acknowledgements 4 iii. How to use this guide 4 1. Content Coverage 5 a. Scope: subjects and languages 5 b. Kinds of research data 5 c. Status of the research data 6 d. Versions 7 e. Data file formats 8 f. Volume and size limitations 10 2. Metadata 13 a. Access to metadata 13 b. Reuse of metadata 13 c. Metadata types and sources 13 d. Metadata schemas 16 3. Submission of Data (Ingest) 18 a. Eligible depositors 18 b. Moderation by repository 19 c. Data quality requirements 19 d. Confidentiality and disclosure 20 e. Embargo status 21 f. Rights and ownership 22 4. Access and Reuse of Data 24 a. Access to data objects 24 b. Use and reuse of data objects 27 c. Tracking users and use statistics 29 5. Preservation of Data 30 a. Retention period 30 b. Functional preservation 30 c. File preservation 30 d. Fixity and authenticity 31 6. Withdrawal of Data and Succession Plans 33 7. Next Steps 34 8. References 35 INTRODUCTION The Policy-making for Research Data in Repositories: A Guide is intended to be used as a decision-making and planning tool for institutions with digital repositories in existence or in development that are considering adding research data to their digital collections. -
JPEG 2000 - a Practical Digital Preservation Standard?
Technology Watch Report JPEG 2000 - a Practical Digital Preservation Standard? Robert Buckley, Ph.D. Xerox Research Center Webster Webster, New York DPC Technology Watch Series Report 08-01 February 2008 © Digital Preservation Coalition 2008 1 Executive Summary JPEG 2000 is a wavelet-based standard for the compression of still digital images. It was developed by the ISO JPEG committee to improve on the performance of JPEG while adding significant new features and capabilities to enable new imaging applications. The JPEG 2000 compression method is part of a multi-part standard that defines a compression architecture, file format family, client-server protocol and other components for advanced applications. Instead of replacing JPEG, JPEG 2000 has created new opportunities in geospatial and medical imaging, digital cinema, image repositories and networked image access. These opportunities are enabled by the JPEG 2000 feature set: • A single architecture for lossless and visually lossless image compression • A single JPEG 2000 master image can supply multiple derivative images • Progressive display, multi-resolution imaging and scalable image quality • The ability to handle large and high-dynamic range images • Generous metadata support With JPEG 2000, an application can access and decode only as much of the compressed image as needed to perform the task at hand. This means a viewer, for example, can open a gigapixel image almost instantly by retrieving and decompressing a low resolution, display-sized image from the JPEG 2000 codestream. JPEG 2000 also improves a user’s ability to interact with an image. The zoom, pan, and rotate operations that users increasingly expect in networked image systems are performed dynamically by accessing and decompressing just those parts of the JPEG 2000 codestream containing the compressed image data for the region of interest. -
D2D: Digital Archive to MPEG-21 DIDL
D2D: Digital Archive to MPEG-21 DIDL Suchitra Manepalli, Giridhar Manepalli, Michael L. Nelson Old Dominion University, Department of Computer Science, Norfolk VA 23508 USA {smanepal, gmanepal, mln}@cs.odu.edu Abstract. Digital Archive to MPEG-21 DIDL (D2D) analyzes the contents of the digital archive and produces an MPEG-21 Digital Item Declaration Language (DIDL) encapsulating the analysis results. DIDL is an extensible XML-based language that aggregates resources and the metadata. We provide a brief report on several analysis techniques applied on the digital archive by the D2D and provide an evaluation of its run-time performance. 1 INTRODUCTION With the increasing trend for digitizing information, technologies are invented to support the growth of digital information. Technologies were adopted, modified and relinquished in a short span, thereby resulting in a huge loss of digital information. The goal of digital preservation is to enable access mechanisms over digital information, by making it transparent to changing technologies over a period of time. Various analysis techniques on digital content exploring its context, provenance and reference are necessary to understand and effectively preserve it. Digital Archive to MPEG-21 DIDL (D2D) is a tool for applying various analysis techniques on a digital archive and producing a Digital Item Declaration Language (DIDL). MPEG-21 is an open standards-based multimedia framework designed to support interoperability of content across communities [8]. MPEG-21 DIDL is XML based language designed to define digital items. DIDL schema is extensible and allows the integration of metadata with the digital items. The tool analyzes the different digital items present inside the archive by applying various techniques on the bit stream of each digital item. -
Part 2. Detailed Matrix (Multi-Page)
Federal Agencies Digitization Guidelines Initiative Raster Still Images for Digitization A Comparison of File Formats Part 2. Detailed Matrix (multi-page) This document presents the information on multiple, easily printable pages. Part 1 provides the same information in a unified table to facilitate comparisons. Revised, September 2, 2014 The FADGI Still Image Working Group http://www.digitizationguidelines.gov/still-image/ Raster Still Images for Digitization: A Comparison of File Formats This is the template for the pages that follow. The reference is the detailed matrix in part 1 of this document set. ATTRIBUTE: Scoring conventions: Questions to Consider: TIFF Common TIFF, Uncompressed Common TIFF, Lossless Compressed GeoTIFF/BigTIFF, Uncompressed GeoTIFF/BigTIFF, Compressed JPEG 2000 JPEG 2000: JP2 JPEG 2000: JPX JPEG JPEG (JFIF with EXIF) PNG PNG PDF PDF (1.1-1.7) PDF/A (1, 1a, 1b, 2) GeoPDF* * GeoPDF refers to either TerraGo GeoPDF or Adobe Geospatial PDF 2 ATTRIBUTE: Sustainability Factors: Disclosure Scoring Conventions: Good, Acceptable, Poor Questions to Consider: Does complete technical documentation exist for this format? Is the format a standard (e.g., ISO)? Are source code for associated rendering software, validation tools, and software development kits widely available for this format? TIFF Common TIFF, Good Uncompressed Common TIFF, Good Lossless Compressed GeoTIFF/BigTIFF, Good Uncompressed GeoTIFF/BigTIFF, Good Compressed JPEG 2000 JPEG 2000: JP2 Good JPEG 2000: JPX Good JPEG JPEG (JFIF with EXIF) Good PNG PNG Good -
PREMIS Data Dictionary for Preservation Metadata, Version
Data Dictiona ~ for Preservation Metadata Contents: Acknowledgments version 3.0 Introduction June 2015 Background The PREMIS Data Model General Topics on Structure & Use Implementation Considerations The PREMIS Data Dictionary Version 3.0 Special Topics Glossary PREMIS Data Dictionary for Preservation Metadata version 3.0 PREMIS Editorial Committee June 2015 – Revised November 2015 http://www.loc.gov/standards/premis CONTENTS CONTENTS .................................................................................................................... iii Acknowledgments ........................................................................................................... v PREMIS Editorial Committee members .................................................................. v Special thanks ........................................................................................................ v PREMIS Web Sites and E-Mail ..................................................................................... viii Introduction ..................................................................................................................... 1 Background ................................................................................................................. 1 Development of the original PREMIS Data Dictionary ............................................ 1 Implementable, core preservation metadata ........................................................... 2 PREMIS Maintenance Activity ............................................................................... -
Extraction of Technical Metadata for Longterm Preservation
Extraction of technical Metadata for longterm preservation : The work with JHOVE in the “kopal” project by Matthias Neubauer, Die Deutsche Bibliothek, Software developer for project “kopal“ January 2006 Table of contents Introduction ................................................................................................................. 3 Some facts about JHOVE ........................................................................................... 4 What JHOVE is ........................................................................................................... 5 What JHOVE is NOT .................................................................................................. 6 How JHOVE is used in koLibRI .................................................................................. 7 Experiences with JHOVE in project “kopal” ................................................................ 9 Supported file formats and future plans .................................................................... 10 Conclusions .............................................................................................................. 12 2 Introduction The “kopal”1 project, founded by the BMBF2, has the goal to create a consistant long term preservation system for digital informations. This system is based on the gathering and creation of descriptive and technical metadata for the objects to archive. The software koLibRI®3, which is a result of the work in project “kopal”, integrated the JHOVE4 software to obtain the technical -
PUID (PRONOM’S Persistent Unique Identifier) • Method (Signature, Extension Oder Container)
Ach, wie gut, dass niemand weiß, wie mein Dateiformat nun heißt DROID und JHOVE Yvonne Tunnat Digitale Langzeitarchivierung 106. Deutscher Bibliothekartag 30.05.2017 Die ZBW ist Mitglied der Leibniz-Gemeinschaft Überblick Theoretischer Teil • Warum Formaterkennung? • Vorstellung DROID (Digital Record Object Identification) • Warum Formatvalidierung? • Vorstellung JHOVE (JSTOR/Harvard Object Validation Environment) • nestor – das deutschsprachige Kompetenznetzwerk zum Thema digitale Langzeitarchivierung • Die nestor-AG Formaterkennung Hands-On-Teil: • Formaterkennung mit DROID • Formatvalidierung mit JHOVE: PDF, TIFF, JPEG • Bonus: Mögliche Zuarbeit zu PRONOM Seite 2 Warum Formaterkennung? • Pro Jahr kommen ca. 90 Dateiformate dazu (Quelle) • Das Formaterkennungstool mit der größten Formatbibliothek (TrID) unterscheidet zwischen 8500 verschiedenen Dateiformaten (TrID beinhaltet nur Binärdateiformate - dazu kommen noch etliche plain/text-Formate) • Dateiformat entscheidet über Anzeige- und Bearbeitungstools (Womit kann ich die Datei öffnen?) • Langzeitarchivierung: Dateiformat ist Grundlage für Risikomanagement und Preservation Planning, um die Langzeitverfügbarkeit zu sichern, beispielsweise durch Migration in aktuelle Dateiformate Seite 3 Was sind Binärdateiformate? Keine reine Textdatei. Enthält nicht nur druckbare Zeichen. Enthält Bitmuster, ggf. Bilder etc. Ansicht Plain-Text Datei im Texteditor Ansicht Binärdatei (Word) im Texteditor Seite 4 DROID • Erstmals veröffentlicht 2006 • Nutzt die PRONOM Signature Files (aktuell 1447 verschiedene -
AIHT) Final Report of the Harvard University Library Originally Issued 2005-04-22 Revised 2005-10-27
Archive Ingest and Handling Test (AIHT) Final Report of the Harvard University Library Originally issued 2005-04-22 Revised 2005-10-27 Introduction The Archive Ingest and Handling Test (AIHT) project was organized by the Library of Congress (LoC) in part to test a key assumption of the evolving National Digital Information Infrastructure and Preservation Planning (NDIIPP) infrastructure;1 namely, that significant bodies of digital content can be easily transferred without loss between institutions utilizing radically different preservation architectures and technologies. The significant areas of Harvard’s interest in the AIHT project included: • An opportunity to make enhancements to the HUL production preservation repository, the Digital Repository Service (DRS) <http://hul.harvard.edu/ois/systems/drs/>. The DRS has been in operation for over 4 years with a policy that limits deposit to objects created by known workflows, in a small set of approved formats, and accompanied by reliable preservation metadata. As the repository policy evolves towards that of an open institutional repository, the AIHT project presented the opportunity to investigate issues surrounding deposit of arbitrary content of unknown provenance. These enhancements included: o A tool to generate repository Submission Information Packages (SIP) packages automatically. This tool is based on JHOVE, the JSTOR/Harvard Object Validation Environment (pronounced “jove”) <http://hul.harvard.edu/jhove/>, which was enhanced to support the AIFF, WAVE, and HTML formats as part of the project. o Identification of deposit-time constraints on object format and technical metadata values. These constraints enforce the technical specifications built into existing workflows. In an environment in which digital objects of unknown provenance are accepted, these constraints need to be removed.