Targeted Language Resources for the Digitisation of Historical Collections

IMPACT is supported by the European Community under the FP7 ICT Work Programme. The project is coordinated by the National Library of the Netherlands.

Targeted Language Resources for the
Digitization of Historical Collections

Annette Gotscharek, Ulrich Reffle, Christoph Ringlstetter,
Klaus U. Schulz
CIS, University of Munich
JISC Workshop 2009, Bath, UK


Questions and Methods

 For historical documents of a specific period: what
kind of linguistic resources?
 What kind of improvements can be expected?
 Consequences for engeneering and processes?
----------------------------------------------------------------------
 (1) Corpus analysis
 (2) Quantitative Experiments on OCR and IR

2


Survey

 1. Special Challenges to Digitize Historical Materials
 2. Composing and Analyzing a Historical Corpus
 3. Types of Linguistic Resources
 4. Evaluation of Benefits: OCR, IR
 5. Consequences for Engineering

3


0. CIS within IMPACT

• (1) Text Recognition: Adaption of Optical Character
Recognition to historical documents
• (2) Resources Building: Enrichment of texts to Improve
Information Retrieval (IR) on historical documents
• (3) Research beyond IMPACT: steps to a next generation
interface to access collections of historical documents


Digitization Projects for Historical Materials

SELECT Create a Historical Collection
SCAN Create Images: Greyscale, Color, QA
PROCESS Improve Images
OCR/Type Create a Symbolical Representation, QA
INDEX Process a Term-Document Representation
PRESENT Provide a User Interface for Access

5


1. Special Challenges for the Digitization of
Historical Materials

1500 1600 1700 1800 1900

• Imaging Damages on Originals
• Optical Character Recognition Rate of Recognition Errors
• Information Retrieval Historical Variants
• Human Reading Unknown Words

date footertext


Optical Character Recognition: Gothic, good quality

Städte den römischen mumcizmg gleich zu stellen. Allem wenn sich je in
einem Rechtstheile die altrechtlichen teutschen Gewohnheiten, und Gesetze
erhalten haben, so ist es gewiß in dieser Lehre, man mag entweder auf die
Befugniß, die Stadtgerechtigkeit zu ertheilen , oder auf die innere
Regimentsverfftssung so-


Optical Character Recognition: medium quality

Fürsten zu Gstternwerden/wer wollte vermainen / daßwt
IhroKhurftrstl Durchl gnädiglsterHcttVatterinderpictcr
rndFrombkcltallmFürstenvorzusetzen!scyn/vnd das halst>
in^cclcQ^ vci pluz^uäzn 5accr6o5 daß tl iN KilchkN GottW
wehr als ein Priester .


Examples of Noise introduced by imperfect OCR

(1) processed word images may lead to False Friends
Fischerei - Tischlerei: F->T, h -> hl (Engfishery - carpenterry)
(2) processed word images may relate to no word at all

^.uglltt. schreibet/
(3) severe word segmentation errors

vndExcmpelFürstl-vnd HeroischerTuzenF

OCR on Gothic materials:
good (WER < 10%); medium (10-30%); bad (< 30%)


Why is it so bad

(1) The image quality is challenging, further
processing needed

(2) The classifiers of the OCR disregard certain type
faces used in historic print

(3) The language resources of the OCR are
inappropriate: historical language


IR: Search Problem
even for keyed collections Kreüter

? krauter

Kräuter
(Eng herbs) kreuter

0 Results for Kreuter
creuther

– kräuter (= Engherbs) as kraͤuter, kreuter, kreüter, kreuter, creuther


Special challenge for IR and OCR on
historical Texts: Spelling variation
 Missing normalization of orthography leads to plenty
of spelling variants in historical documents, e.g. in
German texts (1500-1850):
– teil (= Engpart) as theil, teyl, theyl
– kräuter (= Engherbs) as kraͤuter, kreuter, kreüter,
kreuter, creuther
– fragte (= Engasked) as frug, fruk
 User is not aware of the variants and misses many
documents: sometimes even false friends
 Solution: Mapping from variants to modern lemma
12


Language Resources to tackle challenges
encountered in OCR and IR

• Lexica for OCR
• Language Models for OCR
• Statistical Information about transformation patterns
• Historical Stopwords for IR
• Normalization Lexica with a mapping between modern and
historical wordform for IR
• Syntactical Information for paradigmatic expansion and
disambiguation at POS level

date footertext


Mantra: All Language Resources are Corpus
Based

Possible sources:

• Keyed Materials on the Web
• Non Public Electronic Corpora
• Keying/corrected OCR of Image Corpora
• Noisy OCR Corpora


Status of German historical Corpora

1. Main development corpus
•Proofread texts from 1400 to 1900,
•Medium size: 2.7 Mill. tokens
•For lexicon construction
•For diachronic analysis/classification of vocabulary of distinct periods

2. OCR corpus for lexicon testing
•OCRed Images + groundtruth aligned
•Texts from 16th, 18th, 19th century (5034, 2659, 18052) tokens

3. IR test corpus for lexicon testing
•Special linguistically annotated groundtruth
•Texts from 16th, 17th, 18th, 19th century 31080 tokens


Language in a historical corpus for German
Modern lexicon (CISLEX): coverage on Main Corpus on 10 periods


Compounds (modern components); coverage on Main Corpus on 10 periods


Two Variants of Lexica for IR and OCR
 Hypotetical Lexicon:
Trying to map input strings to modern lexicon entries
in a dynamic way via a special approximate matching
procedure using historical transformation patterns.

 Witnessed Lexicon:
Corpus checked lexicon entries:
Historical spelling variant + modern Lemma for IR
Historical word list for OCR

18


Hypothetical Lexicon: approximate
matching procedure
 Many of the spelling variations can be traced back to
a modern word by applying characteristic patterns /
rewrite rules
– e.g. the historical string theyle can be traced to
its modern equivalent teile by applying th → t
and ey → ei.
 Required resources:
– Contemporary lexicon with inflected word forms
– Set of typical language-specific spelling variation
patterns
19


Approximate matching procedure
Modern lexicon

Inflected Lemmatizing
forms information
… …
teile teil (= part)
... teilen (= to share)
taille taille (= waist)
fragte fragen (= to ask)
… …

20


~ 140 Modern lexicon
Patterns
… forms information
th → t … …
ei → ai teile teil (= part)
ey → ei
l → ll
…
… …

21


Spelling ~ 140 Modern lexicon
variation Patterns
th → t … …
theile ei → ai teile teil (= part)
ey → ei
l → ll
…
… …

22


variation Patterns
th → t … …
ey → ei
l → ll
…
… …

23


variation Patterns
th → t … …
ey → ei
l → ll
…
… …

24


variation Patterns
th → t … …
ey → ei
l → ll
…
… …

25


variation Patterns
th → t … …
frug ei → ai teile teil (= part)
ey → ei
l → ll
…
… …

26


variation Patterns
? th → t … …
frug ei → ai teile teil (= part)
ey → ei
l → ll
…
… …

27


 Advantages:
– No manual work needed
– Dynamic approach

 Limitations:
– Mismatches may link a historical spelling
variation to a wrong modern word.
– A part of the historical vocabulary cannot be
reduced to a modern word by simple matching

28


Hypothetical lexicon; coverage on Main Corpus on 10 periods


Manually collected special lexica
Spelling Modern lexicon
variation
forms information
theile … …
teile teil (= part)
frug taille taille (= waist)
… …

30


Spelling Manual Modern lexicon
variation mapping
forms information
theile … …
teile teil (= part)
frug taille taille (= waist)
… …

31


 Advantages:
– Associations between historical variant and
modern lemma are safe
– Associations that are not covered by the matching
approach can be stored explicitly
 Limitations:
– Time consuming, labor-intensive, situations
occur where specialists (historical linguists) are
needed.
– Hardly ever complete because of immense
number of spelling variants
32


Evaluation of the approximative matcher –
is the lexicon redundant?

 Few empirical studies on crucial decisions for
IR and OCR on historical texts:
– Is a matching approach enough?
– Do we need a lexicon, and if so, in which
scenarios?

33


Lexica built
both assign modern lemmas to historical full forms

 Validated lexicon constructed from Main Corpus
currently ca. 15,000 entries (“kernel lexicon for hist. German”) poor
coverage
Witnessed lexicon for OCR: from Main corpus, 200,000 tokens without
modern correspondence still limited coverage: corpus size
 Hypothetical lexicon for IR”: matching procedure mapping historical full
form to modern pendant plus lemmatizer for modern language (historical
full form <---> modern lemma), based on 140 patterns theoretically 100
Mio entries. High coverage, assignments can be erroneous, only able to
capture regular correspondences (pattern based)


Evaluation of OCR Results

 Test Corpus for 16th , 18th , and 19th century
 Development version of a professional OCR engine with an external
dictionary interface
 Experiments with different lexicon settings
No additional lexicon, character model only
German modern lexicon
corpus based witnessed lexicon
hypothetical lexicon


16th century 18th century 19th century
Reduction Reduction Reduction
Dictionary No. of word No. of word No. of word
of error rate of error rate of error rate
errors errors errors

No Lexicon 1306 - 827 - 2074 -

Optimal
756 42% 395 52% 612 70%
Lexicon
Modern
1096 16% 501 39% 888 57%
Lexicon

W.Historical
938 28% 481 42% 856 59%
Lexicon

Modern +
1011 25% 480 42% 849 59%
Virtual H.L.

WER > 50% WER ~ 10%
36


Evaluation of the approximate matcher in an
IR scenario

 proofread documents from 16th, 17th, 18th and 19th
century and tagged each token manually.
 Collected a list of historical) stopwords
 Defined “precision” and “recall” for our scenario.

37


Main insights IR experiment
 18th and 19th century:
– Pure matching approach leads to good
precision values.
– Recall values are acceptable

38


Main insights IR experiment
 16th and 17th century:
– Precision of the matching approach poor, a
lexicon will help to avoid wrong matches.
– Recall values show that a large number of words
can only be explained by a special lexicon

39


Answer to our refined Question
Is the lexicon redundant for OCR/IR on historical
texts?
 Depends on the material, especially on the date of
origin of the collection:
– Matching approach leads to acceptable results for 19th
and 18th century collections.
– Serious limitations for 16th and 17th century collections.
Special lexica will lead to important improvements
 Combination of matching approach and manually
collected lexica may lead to optimal results.
 For postprocessing validated lexica are needed

40


Engineering Consequences
A Focus Collection of the BavarianStateLibrary
VD16: Collection of Early High German Books

 Collaborative project with BSB on OCR/IR for this
collection: Clemens Neudecker/Fedor Bochow
 Special lexicon building needed
 No 16th century electronic corpora available for
lexicon development
 For real world test we defined a topic area as main
interest: Theology
41


Iterative Process with Bavarian State Library to
Create Resources for VD16 Collection

(1) A random selection of 200 pages from 100 sources
(2) OCR and corpus experiments
(3) Selection of usable sources
(4) Specification of keying by BSB/CIS for 70 complete
books usable for both presentation and linguistic
resources building
(5) Contract with service providers
42


A Focus Collection of the BavarianStateLibrary
VD16: Collection of Early High German Books with
30 million pages

Integrate OCR Supplier: Special Type Face
Models; Character Models

Improve OCR with a specialized historical
Liguistic Database lexicon
for VD16
Improve IR access with a normalization lexicon

43


Wrap Up
• For challenging historical materials specialized
lexica are needed
• Special lexica directly implemented into basic OCR
lifts OCR quality significantly. For bigger projects
seek direct collaboration with OCR partners
• For IR: use approximative matching or normalization
lexica to process user queries
• Integrate research institutions and collection
holders

Targeted Language Resources for the Digitisation of Historical Collections

Recomendados

Recomendados

Más contenido relacionado

Destacado

Destacado (8)

Similar a Targeted Language Resources for the Digitisation of Historical Collections

Similar a Targeted Language Resources for the Digitisation of Historical Collections (20)

Último

Último (20)

Targeted Language Resources for the Digitisation of Historical Collections