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Supporting PDF accessibility evaluation:early results from the FixRep project

Andrew Hewson & Emma Tonkin

a.hewson@ukoln.ac.uk

e.tonkin@ukoln.ac.uk

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Introduction

UKOLN:Based at the University of Bath in the UK is:"A centre of excellence in digital information

management, providing advice and services to the library, information and cultural heritage communities.”

FixRep:An 18 month project aiming to examine

existing techniques and implementations for automated formal metadata extraction, with the aim of enabling metadata triage

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What is formal metadata?Formal metadata;• Includes information such as filetype, title,

author and image captions• Is mostly intrinsic to the document and its

citation.• Could it include information of relevance to

accessibility?

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What is accessibility?Web accessibility means that people with

disabilities can use the Web.(http://www.w3.org/WAI/intro/accessibility.php)

capable of being reached;capable of being read with comprehension;easily obtained;easy to get along with or talk to; friendly; (http://wordnetweb.princeton.edu/perl/webwn)

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PDF format

Web-based uses of relevance to digital libraries for example include:

forms printable versions of resources pre-prints of papers and articles.

A very common format found in institutional repositories.

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Document accessibilityCan we aspire to a perfectly accessible

repository?

Careful editing / repository management takes time and is labour intensive for administrators and users.

Finding a balance between quantity and quality, i.e. maximising usability of repository content, is the realistic goal.

Not strict validation, but support for user level review / triage.

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Research questions

What span of content appears in a document repository that enables user deposit?

Does this variation in document format imply a reduction in accessibility, what sort of reduction, to whom, and to what extent?

Is it possible for us to automatically identify issues that may be of particular concern, or for us to identify good practice where it is used?

Separating non-optimal features from show-stopper problems.

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Methodology #1: PrototypeA prototype has been developed for analysis of

PDFs. This extracts information about the document in a number of ways:

• Header and formatting analysis• Information from the body of the document• Information from the originating filesystem

Based on Unix tools the prototype has been developed in Perl using pdfinfo, pdftotext, and pdfimages, as well as a number of CPAN modules.

It uses a REST service API

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Methodology #2: Pilot Case StudyOPUS Repository (University of Bath)

Spidered site to identify PDFsPDFs cached offlineAnalysed via batch processResponses placed in MySql databaseData analysis process completed manually via

SQL queries.Automation of analysis process goal for future

iterations of project.

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Results

Proportion of documents successfully processed

80% were successfully batch processed with the results stored in the database

The 20% that failed exhibited two categories of errors:

1. No metadata was available for extraction

2. Format of file unsupported by toolset

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Results

XML Tag useSmall number of tags used

(26)Usage was consistent

(average 21, mode 21)Some ‘traditional’ tags were

absent in most cases(author, title, etc.)

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Results

PDF VersionsMost popular version seems

to be 1.4 – however this might be attributable to the ‘Creator’ software used to generated the PDFs in the sample: in particular due to the addition of a ‘cover sheet’ before being added to the OPUS repository.

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Results

‘Producer’ and ‘Creator’

helloThese two tags both show disproportionate favouritism for two applications (compared with an expected normal distribution)

It is likely, as with the favoured PDF version, that is an artefact of the cover sheet addition to the PDFs.

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Discussion

The ‘cover sheet’ issueAs mentioned, a cover

sheet has been prepended to many of the PDFs examined.

This might not seem to be an issue, however, as can be seen here it might confuse automated systems, rendering the metadata virtually useless

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Conclusions

Good news! More tagged PDFs around than expected.

Bad news! We may ‘shooting ourselves in the foot’ with additions like after-the-fact cover sheets. This may remove original metadata that could have been utilised for machine learning.

This prototype tools has already proved very useful and we plan to develop it further.