Digitization and enhancement of biodiversity literature through OCR, scientific names mapping and crowdsourcing

Chris FreelandTechnical Director,

Biodiversity Heritage Library

BioSystematics Berlin 201122 Feb 2011

http://biodiversitylibrary.org/page/33061402

Digitization

http://biodiversitylibrary.org/page/6165462

Workflow

Selection Preparation

Post Production(Re)publication

Digitization

Conservation

Scanning Derivatives

• XML• JP2

• PDF• JPG• TXT• DJVu

Master Derivatives

PDF

OCR

XML

JP2

Storage

Files are stored & sync’d across

BHL clusters

Optical Character Recognition (OCR)

http://biodiversitylibrary.org/page/2836705

OCR is a *BIG* challenge

• All book / literature digitization projects affected, not just BHL

• Especially problematic in BHL– More than 50 languages represented in BHL– Dates of publication from 1400’s to 2000’s– Irregular typeface / typesetting– Multiple languages on one page

• Botanical descriptions in Latin

Abbild ungen und Beschreibungen der

Fische Syriens, nebst

einer neuen Classification und Characteristik sämmtlicher Gattungen

der i

JOH. JAKOB HECKEL, Inipectoi am k. k. Hof-Natur.-iUenkabinete in

Wien, mehr, yelelirt. UeHtllMeii. MIfglivd.

STUTTGART. E. Schweizerbart' sehe Verlagshandlung,

1843.

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2007 Name Finding Study

>35% OCR error rate for names only

1 Insert Space 8 n->v

2 Omit Space 9 l->i

3 e->c 10 r->i

4 u->I 11 u->ii

5 u->n 12 h->l

6 i->l 13 h->ii

7 c->e 14 e->o

Top OCR errors

35.16%

Of the 3,003 names, 1,056 were incorrectly transcribed by OCR.

Wei, et al. An Evaluation of Taxonomic Name Recognition (TNR) in the Biodiversity Heritage Library. Proceedings of TDWG. 2008.http://www.tdwg.org/proceedings/article/view/380

• WikiSource• Trove - National Library of Australia

Manual techniques for text correction

WikiSource Example

http://biostor.org/wiki/Page:Spixiana1999zool.djvu/293

Goal: Semi-automated text correction

• OCR + Machine Learning + Users– Let machines do raw processing– Develop algorithms for natural language

processing & machine learning– Build a community of (human) users to help

• reCAPTCHA as an example– Why not just use reCAPTCHA?

• Google bought it

*More work needed here*

Scientific names mapping

http://biodiversitylibrary.org/page/27782237

Image from ScannerConverted to text via OCRName finding via TaxonFinder Extract namesSubmit to NameBankTaxonFinder API response

Name Finding in action

with uBio’s TaxonFinder…

Crowdsourcing

http://biodiversitylibrary.org/page/20965795

CiteBank: http://citebank.org

• New search index to BHL content

• Platform for journals/publishers/societies in need of tools to store & share their digitized content

• Access to “crowdsourced” articles from BHL scans

Crowdsourcing Statistics & Analysis

• Analysis– http://biodiversitylibrary.blogspot.com/2009/04/p

df-article-metadata-analysis.html– At that time, more than 80% of the PDFs created

had metadata attached by users• More than 50% contributed accurate article-level

information

• New analysis over more data this summer / fall– Now have more than 58,000 PDFs to analyze

Open Data = More Use

• Scholars– Rod Page

• iPhylo• BioGUID• BioStor

– Ryan Schenk• Other Apps

– EarthCape– ZipecodeZoo

Conclusion

• BHL is a massive dataset useful for multidisciplinary research– Systematics– Natural Language Processing– Humanities

• BHL is open– Free to use at http://biodiversitylibrary.org– Open access data for scholarly use & reuse

• BHL has APIs and data exports to enable reuse– BHL data can be incorporated into other virtual research

environments (EOL, Scratchpads, BioStor, others)

Questions?

Chris FreelandTechnical Director, Biodiversity Heritage LibraryDirector, Center for Biodiversity Informatics, Missouri Botanical Garden Missouri Botanical Garden4344 Shaw Blvd.St. Louis, MO 63110 USA

BioSystematics Berlin 201122 Feb 2011

Email: chris.freeland@mobot.org

Twitter: @chrisfreeland

Blog / info: chrisfreeland.com