DH101 2013/2014 course 7 - OCR, Printed text recognition, Handwriting recognition, Ornaments classification, Named entities disambiguation

Digital Humanities 101 - 2013/2014 - Course 7
Digital Humanities Laboratory
Andrea Mazzei and Fr´d´ric Kaplan
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andrea.mazzei,frederic.kaplan@epﬂ.ch

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A Job offer
• Running an OCR transcription of 320 pages
• about 60 hours of work
• 25 CHF / hour.

Digital Humanities 101 - 2013/2014 - Course 7 | 2013

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Results of the peer grading process


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New projects


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Venitian opera staging and machinery
• A project that ﬁnd way for better understanding and visualizing opera staging
based on evidences found in historical sources (treatise, music prints, etc.)
• Rosand, E. 1990. Opera in Seventeenth-Century Venice : The Creation of a Genre.
Berkeley : University of California Press.
• Bjurstr¨m, P. 1962. Giacomo Torelli and Baroque Stage Design. Stockholm :
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Almqvist and Wiksell.
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• Leclerc, H. 1987. Venise et l’av`nement de l’op´ra public A l’ˆge baroque. Paris :
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A. Colin.
• Larson, O. K. 1980. Giacomo Torelli, Sir Philip Skippon, and Stage Machinery for
the Venetian Opera, Theatre Journal, Vol. 32, No. 4, pp. 448-457.
www.jstor.org/stable/3207407

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Venetian storytelling in the Middle-Age
• Marin Sanudo was an historical writer. In contrast to others writer of the
epoch, he wrote a diary noting all the events happend in Venice. Of
course it is not the only one diary wrote in Venice. Imagine how to use
this personal information.


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Looking at music printing typefaces
• A project that looks at the diﬀerent music typefaces used in Venetian
prints. Typical questions are : the size of the typeface, when they were
used, for what repertoire, what printers used them, etc.
• Agee, R. 1998. The Gardano Music Printing Firms, 1569-1611.
Rochester, University of Rochester Press.
• Bernstein, J. 1998. Music Printing in Renaissance Venice. The Scotto
Press (1539-1572). Oxford, Oxford University Press.
• Bernstein, J. 2001. Print Culture and Music in Sixteenth-Century Venice.
Oxford, Oxford University Press.


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Music at San Marco
• A project that can look at how the capella di San Marco evolved over
time : how many musicians, where they played in the Basilica, what they
played, etc.
• Selfridge-Field, E. 1994. Venetian instrumental music from Gabrieli to
Vivaldi. New York : Dover.
• Moretti, L. 2004. Jacopo Sansovino and Adrian Willaert at St Mark’s,
Early Music History, Vol. 23, pp. 153-184.


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Venetian music prints in libraries today
• A project that looks at the production of music prints in Venice and
where they are hold today in libraries and archives around the world
• The Repertoire International des Source Musicales, Series A/I on music
prints. http ://www.rism.info [will be made available digitally for the
project]


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Semester 1 : Content of each course
• (1) 19.09 Introduction to the course / Live Tweeting and Collective note
taking
• (2) 25.09 Introduction to Digital Humanities / Wordpress / First assignment
• (3) 2.10 Introduction to the Venice Time Machine project / Zotero
• 9.10 No course
• (4) 16.10 Digitization techniques / Deadline first assignment
• (5) 23.10 Datafication / Presentation of projects
• (6) 30.10 Semantic modelling / RDF / Deadline peer-reviewing of first
assignment

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Semester 1 : Content of each course
• (7) 6.11 Pattern recognition / OCR / Semantic disambiguation
• (8) 13.11 Historical Geographical Information Systems, Procedural modelling
/ City Engine / Deadline Project selection
• (9) 20.11 Crowdsourcing / Wikipedia / OpenStreetMap
• (10) 27.11 Cultural heritage interfaces and visualisation / Museographic
experiences
• 4.12 Group work on the projects
• 11.12 Oral exam / Presentation of projects / Deadline Project blog
• 18.12 Oral exam / Presentation of projects

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Today's course
• Printed Text Recognition
• Hand Writing Recognition
• Ornament Recognition
• Text Mining and semantic disambiguation : Extracting named entities
(people, places, etc.) in a text using Wikipedia


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Part I : Printed Text Recognition


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OCR : Optical Character Recognition
A system that provides a full recognition of all the printed characters by
simply scanning the support.


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Mori et al. (1992). Historical review of OCR R&D
• 1940 : The ﬁrst version of OCR
• 1950 : The ﬁrst OCR machines appear
• 1960 - 1965 : First generation OCR : NOF, Farrington 360, IBM 1418.
They all used a special font
• 1965 - 1975 : Second generation OCR : IBM 1287, NEC, Toshiba. They
could also recognize constrained hand-printed alpha-numerals.
• 1975 - 1985 : Third generation OCR : IBM 1975, Poor print quality or
handwritten characters. 275 fonts. Handwriting recognition.
• 1986 - Today : OCR to the people
Eikvil, L. (1993). Optical Character Recognition


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OCR capabilities
The recognition performance depends on the type and number of fonts
recognized.
• Fixed font : the sytem can recognize only one font
• Multi font : the system can recognize multiple fonts
• Omni font : the system can recognize most nonstylized fonts without
having to maintain huge databases of speciﬁc font information


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Omni-font OCR Overview Of Processing


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Preprocessing : Text Lines Straightening

Zhang, Z., & Tan, C. L. (2002, June). Straightening warped text lines using polynomial regression. In Image Processing. 2002.
Proceedings. 2002 International Conference on (Vol. 3, pp. 977-980). IEEE.


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Preprocessing : Character Detection
• Image binarization using local adaptive thresholding

• Character detection using region growing-based methods. PROBLEM !



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Segmentation Problems : Touching and fragmented characters
• Joints will occur if the document is a dark photocopy or if it is scanned
at a low threshold.
• Joints are common if the fonts are serifed.
• The characters may be split if the document stems from a light
photocopy or is scanned at a high threshold


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Segmentation Problems : Distinguishing noise from text
Dots and accents may be mistaken for noise, and vice versa.


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Segmentation Problems : Mistaking graphics for text
This leads to non-text being sent or text not being sent to recognition


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Feature Extraction
From each character several features can be extracted :
• Rasterized pixels
• Geometric moment invariant
• Morphological features


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Feature Extraction : Zoning
MxN zones are computed as average gray level from the image of the
character.

Due Trier, O., Jain, A. K., & Taxt, T. (1996). Feature extraction methods
for character recognition-a survey. Pattern recognition, 29(4), 641-662


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Feature Extraction : Projection Profile


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Feature Extraction : Structural Analysis
Strokes, bays, end-points, intersections between lines and loops.
High tolerance to noise and style variations.


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Classification
The principal approaches to decision-theoretic recognition are minimum
distance classiﬁers, statistical classiﬁers and neural networks.


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Matching


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Optimum statistical classifiers.
• Bayesian classiﬁer. Given an unknown symbol described by its feature
vector, the probability that the symbol belongs to the class c is computed
for all classes c = 1...N. The symbol is then assigned the class which
gives the maximum probability.
• ...


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Post Processing : Grouping
From symbols to strings using symbols proximity


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Post Processing : Error Detection and Correction
• Use of rules deﬁning the syntax of the word. Ex. In English the k never
appears after the h.
• Use of dictionaries. If the word is not in the dictionary, an error has been
detected, and may be corrected by changing the word into the most
similar word.


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Self-learning
Modern OCR systems enlarge the database of characters when new fonts
are encountered. Character recognition is based on the database previously
built in, which contains the important features related to the characters
which are known already. It is necessary that this database is able to self
expand as more and more new characters are met in order to increase the
recognition ability.


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Handwriting Recognition (HWR)


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Offline HWR : Many difficult problems
• Stroke ordering

• Broken lines

• Merged blobs


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From Offline to Simulated Online

It is not reliable
• What order were the strokes written in ?
• Doubled-up line segments ?
• Ink blobs ?
• Spurious joins between letters ?
• Missing joins ?

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Segmentation : Strokes Extraction


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Segmentation : Segments Fitting
Robustly cut letters into segments
Match multiple segments to detect letters
Easier than matching whole letter

Hutchison L. Handwriting Recognition for Genealogical Records - Course 7 | 2013
Digital Humanities 101 - 2013/2014

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Analytical Approach
It treats a word as a collection of simpler sub-units such as characters
• Segmentation of the word into these units
• Identiﬁcation of the units
• Word-level interpretation using a predeﬁned lexicon


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Problems with the Analytical Approach
• segmentation ambiguity : deciding where to segment the word image

• variability of segment shape : determining the identity of each segment


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Holistic Matching
Treats the word as a single, indivisible entity and attempts to recognize it
using features of the word as whole.


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Advantages of the Holystic Matching
Coarticulation eﬀect, i.e., the changes in the appearance of a character
as a function of the shapes of neighboring characters


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Orthogonality of holistic features : information about the word that
is clearly orthogonal to the knowledge of characters in it and it stands to
reason that the introduction of this knowledge should improve recognition


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Evidence from psychological studies : psychological studies of
reading points towards the fact that humans do not, in general, read words
letter by letter.


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Dynamic Global Search
Assemble word spelling from possible letter readings


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Result 1


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Result 2


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Result 3


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ABBYY Fine Reader : A Case Study


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Scanned Document


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Image Rotation Adjustment


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Image Rotation Adjustment


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First Extraction


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Synthetizing the Table


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Second Extraction


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Retrieval of the ornaments from the Hand-Press Period


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Problem Statement
For millions of intact books and tens of millions of loose pages, the
provenance of the manuscripts may be in doubt or completely unknown


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Manual Solution
Human experts are capable to regain the provenance by examining
linguistic, cultural and/or stylistic clues.
However, such experts are rare and this investigation is a time-consuming
process.


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Automatic Solution
By comparing the initial letters in the manuscript to annotated initial
letters whose origin is known, the provenance can be determined.
This process can be automatized


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What are the Challenges ?


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Ornament Segmentation
Ornament(s) detection and localization with respect to the page reference system.

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Baudrier, E., Busson, S., Corsini, S., Delalandre, M., LandrA c , J., &
Morain-Nicolier, F. (2009, July). Retrieval of the ornaments from 2013/2014 - Course 7 | 2013
Digital Humanities 101 - the hand-press

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A Compression Based Distance Measure for Texture
The distance between a window and an annotated initial letter is
denoted as :
mpegSize(W , IL) + mpegSize(IL, W )
distCK 1(W , IL) =
−1
mpegSize(W , W ) + mpegSize(IL, IL)
The ﬁrst image supplied to mpegSize is assigned as an I frame
and the second becomes a P frame.
Campana, B. J., & Keogh, E. J. (2010). A compression-based
distance measure for texture. Statistical Analysis and Data
Mining, 3(6), 381-398

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Properties of CK1 Distance Measure
Eﬃcient, robust and parameter-free texture similarity measure.
Rotation, Colour and Illumination Invariant.


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Gabor Filters

Images are convolved with each ﬁlter.
The standard deviation and mean of each response => 48 length vector
Vector Euclidean distance
Wang, X., Ding, X., & Liu, C. (2005). Gabor ﬁlters-based feature extraction for
character recognition. Pattern recognition, 38(3), 369-379

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Data Sets


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Experimental Results


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Part II : Text mining and semantic disambiguation


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Case study : Extracting named entities (people, places,
etc.) in a text using Wikipedia


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Using Wikipedia
• A Unique ID : A Wikipedia article is identiﬁed by a unique name, which is
the article title itself. The respective URL of a Wikipedia article can be
created by concatenating the words in the article title and appending it
to the URL root of the Wikipedia


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Using Wikipedia
• Redirections : Some entities can have multiple names. In order to address
this issue, Wikipedia has some article titles that do not have a
substantive article and are only redirected to a diﬀerent Wikipedia article
with another title. This mechanism is called redirection. Redirections are
used for other purposes such as spelling resolution (e.g. the article title
Oranges is redirected to Orange) and abbreviation resolution (e.g. the
article title UCLA is redirected to University of California, Los Angeles).


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Using Wikipedia
• Disambiguation pages : A disambiguation page is created for ambiguous
entity names and it enumerates all the possible articles for that name. For
example, the disambiguation page for Paris enumerates 25 places called
Paris (in America, Canada and Europe), 33 people having Paris as name
or surname, 10 television series and ﬁlms, whose title contains the word
Paris, etc.


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Using Wikipedia
• Outgoing links : In the body text of the Wikipedia article there are
references (links) to other articles. The references are within pairs of
double square brackets.
• Infobox : An infobox is a ﬁxed-format table designed to be added to the
top right-hand corner of articles to consistently present a summary of
some unifying aspect that the articles share and sometimes to improve
navigation to other interrelated articles.


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3 steps
• Data extraction : A (sequence of) word(s) is extracted from a ”Le
Temps” article (e.g. Le Paris). Set the right boundaries in the extracted
data (e.g. from ”Le Paris” is retrieved the ”Paris” ).
• Disambiguation : Retrieve all the Wikipedia articles whose title contains
the word ”Paris” (e.g. Paris (France), Paris (Texas), Paris Hilton, Paris
(mythology), etc). Find the Wikipedia article that maximizes the
agreement between the content extracted from Wikipedia and the
context of the ”Le Temps” article.
• Entity classiﬁcation : Classify the entity as place, person, company, etc,
based on the chosen Wikipedia article

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Disambiguation strategy


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(1) Data extraction
• The first step is the extraction of possible named entities. This step is
based on the fact that the named entities consist of capitalized words.
The rules that we apply for the extraction of possible named mentions in
the text are the following :
• Retrieve all the capitalized words (e.g. England)
• Retrieve recursively terms T0 of the form T1 Particle T2, where Particle is one of a possessive
pronoun, and the terms T1 and T2 are capitalized words or sequences of capitalized words
(e.g. University of Edinburgh, European Society of Athletic Therapy and Training)
• In French, some entities can contain non-capitalized words, after some specific words.
Therefore, we retrieve non-capitalized words if they are followed by a word that is contained
in a predefined set of words (e.g. Union, Biblioth`que, etc). For example the Union
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sovietique is considered as entity.


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(2) Disambiguation
• The disambiguation process employs a vector space model, in which a
vectorial representation of the processed article is compared with the
vectorial representations of the Wikipedia entities.
• The vectorial representation of the processed article (article vector) is a
vector having all the possible entities of the speciﬁc article obtained
during the previous step, while the vectorial representation of a Wikipedia
article (Wikipedia vector) is a vector having all the outgoing links in the
body text of the article.
• Once a Wikipedia article is identiﬁed as the most similar to the processed
article, the article vector is updated by adopting the features of the
chosen Wikipedia vector.

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(3) Entity classification
• The last step is to classify the entities into persons, places, companies,
etc.
• Ex : It the entity a place ? If the Wikipedia article contains an infobox,
then we retrieve it and we search for speciﬁc tags in it that can classify
the entity as a place.
• If the Wikipedia article does not have an infobox, then we use the ﬁrst
sentence of the body text.


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Partial results
• We have implemented the algorithm and tested it on a subset of the
database
• Our current estimation of the number of entity retrieved is 85 %
• Main issue : Some entites are not in Wikipedia.


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From Wikipedia to Wikipast
• The First principle of Wikipedia is that it is an encyclopedia. Not all
entites are allowed. Sourcing is important but secondary
• On going discussion with Wikimedia to create an alternative to
Wikipedia, allowing page on any person, place, etc. from the past as long
at it is clearly sourced.


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DH101 2013/2014 course 7 - OCR, Printed text recognition, Handwriting recognition, Ornaments classification, Named entities disambiguation

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