=Paper= {{Paper |id=Vol-1624/paper25 |storemode=property |title=Textual Information Extraction in Document Images Guided by a Concept Lattice |pdfUrl=https://ceur-ws.org/Vol-1624/paper25.pdf |volume=Vol-1624 |authors=Cynthia Pitou,Jean Diatta |dblpUrl=https://dblp.org/rec/conf/cla/PitouD16 }} ==Textual Information Extraction in Document Images Guided by a Concept Lattice== https://ceur-ws.org/Vol-1624/paper25.pdf

Textual Information Extraction in Document
Images Guided by a Concept Lattice

Cynthia Pitou12 and Jean Diatta1
1
EA2525-LIM, Saint-Denis de La Réunion, F-97490, France
2
Groupe Austral Assistance, 16 rue Albert Lougnon, 97490 Sainte Clotilde, France
cpitou@gaa.fr jean.diatta@univ-reunion.fr

Abstract. Text Information Extraction in images is concerned with ex-
tracting the relevant text data from a collection of document images. It
consists in localizing (determining the location) and recognizing (trans-
forming into plain text) text contained in document images. In this work
we present a textual information extraction model consisting in a set
of prototype regions along with pathways for browsing through these
prototype regions. The proposed model is constructed in four steps: (1)
produce synthetic invoice data containing the textual information of in-
terest, along with their spatial positions; (2) partition the produced data;
(3) derive the prototype regions from the obtained partition clusters; (4)
build the concept lattice of a formal context derived from the prototype
regions. Experimental results, on a corpus of 1000 real-world scanned
invoices show that the proposed model improves significantly the extrac-
tion rate of an Optical Character Recognition (OCR) engine.

Keywords: textual information extraction, concept lattice, clustering

1 Introduction
Document processing is the transformation of a human understandable data in a
computer system understandable format. Document analysis and understanding
are the two phases of document processing. Considering a document containing
lines, words and graphical objects such as logos, the analysis of such a docu-
ment consists in extracting and isolating the words, lines and objects and then
grouping them into blocks. The subsystem of document understanding builds
relationships (to the right, left, above, below) between the blocks. A document
processing system must be able to: locate textual information, identify if that
information is relevant comparatively to other information contained in the doc-
ument, extract that information in a computer system understandable format.
For the realization of such a system, major difficulties arise from the variability
of the documents characteristics, such as: the type (invoice, form, quotation, re-
port, etc.), the layout (font, style, disposition), the language, the typography and
the quality of scanning. In the literature, works in pattern recognition [16] and
character recognition [28] provide solutions for textual information extraction in
a computer system understandable format. Works in automatic natural language
processing [6] contribute to solving the problem about the detection of relevant
information. This paper is concerned with scanned documents, also known as
document images. We are particularly interested in locating textual information
in invoice images. Invoices are largely used and well regulated documents, but
not unified. They contain mandatory information (invoice number, unique iden-
tifier of the issuing company, VAT amount, net amount, etc.) which, depending
on the issuer, can take various locations in the document. For instance, it seems
difficult to identify a trend as to the position of the date and invoice number.
However, similarities may occur locally for one or many information. To take
an example, the amount is usually positioned at bottom-right in the French and
English systems. Recent approaches such as those presented in [3, 4, 9] are specif-
ically concerned with the extraction of information in administrative documents
such as invoices. These works have in common the search, within a base, for a
document similar to an input document. Each document of this base is assigned
a template that lists some attributes (position, type, keywords) to be used in or-
der to locate information contained in similar input documents. Bartoli et al. [3]
propose a system of selecting, for an input document, the nearest wrapper based
on a distance measure. A wrapper is an object containing information about
geometric properties and textual content of elements to extract. Belaid et al. [4]
propose a case-based-reasoning approach for invoice processing. Cesarini et al.
[9] propose a system to process documents that can be grouped into classes. The
system comprises three phases: (1) document analysis, (2) document classifica-
tion, (3) document understanding.
The present paper is in the framework of region-based textual information lo-
calization and extraction [29, 30]. We present a textual information extraction
model consisting in a set of prototype regions along with pathways for brows-
ing through these prototype regions. The proposed model is constructed in four
steps:

1. produce synthetic invoice data from real-world invoice images containing the
textual information of interest, along with their spatial positions;
2. partition the produced data;
3. derive the prototype regions from the obtained partition clusters;
4. derive pathways for browsing through the prototype regions, from the con-
cept lattice of a suitably defined formal context;

The paper is organized as follows. Section 2 is devoted to the construction of
prototype regions. The formal context defined using the obtained prototype re-
gions, and the determination of paths from the concept lattice of that formal
context are described in Section 3. Section 4 presents our approach for textual
information extraction, using the defined paths. Finally, some experimental re-
sults are presented in Section 5 and the paper is closed with a conclusion and
perspectives.
2 Construction of prototype regions
2.1 Construction of a synthetic data set
The present work is motivated by the request of a company interested in develop-
ing its own system enabling to automatically extract some textual information
from scanned invoices. The company has provided us with a corpus of 1000
real-world scanned invoices, emitted by 18 service providers whose business is
around car towing and auto repair. All the images are one page A4 documents.
The whole set of information the company is interested in, comprises: invoice
number, invoice date, net amount, VAT amount, customer reference, the type of
service provided, the issuer identity. In our study, we consider only the following
five information:
– I1: the key word of the service provided: towing or auto repair,
– I2: customer reference: a string of 9-13 characters,
– I3: the plate number of the assisted vehicle,
– I4: the invoice issuer unique identifier: a string of 14 digits,
– I5: the net amount of money requested for the provided service.
Each of the textual information is located in a region delimited by a rectangle
defined by the coordinates (x, y) (in pixels) of its top left corner and the coor-
dinates (z, q) of its bottom right corner. In the sequel, by the term region will
be meant a rectangular area in an invoice image. Hence, a region may be repre-
sented by the four coordinates (x, y, z, q) of its top left and bottom right corners.
As the information to be extracted are located in (rectangular) regions we adopt
a region-based extraction approach. The regions which the proposed approach
is based on are prototypes obtained from the more specific regions containing,
each, a single information. Now, the coordinates of the regions containing the
needed information are not available for the real-world scanned invoices at hand.
To cope with this, we develop a JAVA program, with a graphical interface, en-
abling to create synthetic invoice data simulating the real-world scanned invoices
along with the approximate coordinates of the specific rectangles containing the
needed information. For instance, from a real-world scanned invoice an initial
synthetic invoice is manually created. This synthetic invoice is a single black
and white A4 page. This page will contain a string corresponding to a plate
number approximately at the same location as the plate number information
appears in the real-world invoice. Additionally, the string will be inserted ap-
proximately with the same size and the same font as in the real-world invoice
in order to look like it. The string is inserted manually in the initial synthetic
invoice as one can do with a text editor. However, many strings contained in
the real-world invoice are not reproduced in the synthetic invoice. For instance,
the information about the emitter and the receiver (address, phone number,
...) are not reproduced because they are not relevant for the study. Thus, the
set of textual information I1 to I5 is placed manually on the synthetic invoice.
Then, from the obtained initial synthetic invoice a fixed number of synthetic
invoice images may be created automatically. In such synthetic invoice images,
the information locations are maintained identically to the initial synthetic in-
voice but the contained string may vary. Finally, for each distinct emitter of the
real-world invoice images corpus, one initial synthetic invoice image is created
manually and a fixed number of synthetic invoice images is created automati-
cally from the initial synthetic invoice. A corpus of 1000 synthetic invoice images
is thus produced and, for each synthetic invoice, both the textual information
and the coordinates of the respective rectangles containing them, are stored in
a database. The original distribution per emitter of the real-world invoices is
preserved in the synthetic corpus of images. Synthetic data sets can then be
generated from this database for closer insight. An example of such data sets
is a set of (synthetic invoice) records described by 20 variables representing 5
blocks of 4 coordinates (x, y, z, q), each block being associated with one of the 5
considered information I1 to I5. It should be noted that this possibility to pro-
duce a synthetic representation of a real-world scanned invoice is an important
step for updating the proposed model, namely when one has to extract informa-
tion from previously unseen scanned invoice. This point will be discussed later
in Section 6.

2.2 Clustering of the synthetic data
As we mentioned in Section 2.1, the regions which our proposed approach is
based on are the so-called prototype regions, obtained from the specific regions
that contain, each, a single information. More precisely, a prototype region as-
sociated with a given information should be a region containing a homogeneous
set of specific regions related to various positions of this information in different
invoice images. This makes cluster analysis methods, (such as the partitioning
ones) good candidates for capturing such homogeneous sets of specific regions.
Then, in the next section, the construction of prototype regions from such ho-
mogeneous sets of specific regions is explained.
The synthetic data obtained from the previous phase can be partitioned either:
(a) in an overall view taking into account all of the 20 variables, or (b) in 5
independent views, each corresponding to one of the 5 information I1 to I5 and
taking into account, for each view, the 4 associated variables. The approach in
five independent views consists in creating five data sets: D1, D2, D3, D4 and
D5. A record in Di is described by the four coordinates of regions containing
information Ii. For both approaches we adopted the K-means [23] clustering
with Euclidean distance. K-means is a popular, simple and efficient algorithm
for cluster analysis. To determine the number of clusters, we conducted, on the
one hand, an (agglomerative) ascending hierarchical clustering with Ward crite-
rion (clustering method based on a classical sum-of-squares criterion, producing
groups that minimize within-group dispersion) [24] and, on the other hand, ex-
ecutions of K-means for values of k between 2 and 18. Several validity criteria,
such as within cluster sum of squares, silhouette and Calinkski-Harabasz [33],
provided by the package clusterCrit of R software, were used to determine the
optimal number of clusters for each data set. It turns out that considering five
independent views leads to better clusters w.r.t. each of the considered validity
criteria. Therefore, we adopt the option consisting in partitioning each of the 5
views. The best values of k obtained for the respective five views are shown in
Table 1.

Table 1. Number of clusters for data sets D1, D2, D3, D4 and D5.

D1: Info I1 D2: Info I2 D3: Info I3 D4: Info I4 D5: Info I5
k 10 10 10 3 10

2.3 Determination of the prototype regions
As we mentioned in the previous section, a prototype region associated with
an information should contain a homogeneous set of specific regions related to
various positions of this information in different invoice images.
Recall that for each information Ii, the associated data set Di is partitioned into
some number of clusters (see Table 1). Then, we associate to each of these clus-
ters, say C, a prototype region defined as the smallest rectangle R containing each
of the specific rectangles in C. Thus, we obtain 43 prototype regions R1 ,...,R43 ,
with the first 10 related to information I1, the next 10 to I2, the next 10 to I3,
the next 3 to I4 and the last 10 to I5. Figure 1 shows the prototype regions
related to information I4. The next step of the construction of our proposed
model is to set up pathways for efficiently browsing through the set of defined
prototype regions. Such pathways will be obtained from the concept lattice of a
suitably defined formal context.

3 Determination of pathways for browsing through the
prototype regions
So far, we indicated how we determine prototype regions containing the textual
information to be extracted. So we come to the fourth step in our approach,
namely, define pathways for efficiently browsing through the set of these proto-
type regions. For this, Formal Concept Analysis (FCA) appears very appropriate.
Indeed, the pathways we seek to determine may be obtained from the concept
lattice of a suitably defined formal context.

3.1 Construction of the concept lattice
Recall that a formal context is a triple K = (O, A, R), where O is a set of objects,
A a set of attributes and R ⊆ O × A a binary relation from O to A. A formal
concept of K is a pair (X, Y ) such that Y = X 0 = {a ∈ A : xRa for all x ∈ X}
and X = Y 0 = {x ∈ O : xRa for all a ∈ Y }. Note that the double application of
Fig. 1. Prototype regions related to information I4.

the derivation operator (.)0 is a closure operator, i.e. (.)00 is extensive, idempotent
and monotone. Sets X ⊆ O, Y ⊆ A, such that X = X 00 and Y = Y 00 are said to
be closed. The subset X ⊆ O is called the extent of the concept (X, Y ) and Y its
intent. The concept lattice of the formal context K [35], also known as the Galois
lattice of the binary relation R [2], is the (complete) lattice (L(K), ≤), where
L(K) is the set of formal concepts of K and ≤ the subconcept/superconcept
partial order. Thus, a concept lattice contains a minimum (resp. a maximum)
element according to the relation ≤, called the bottom (resp. the top). In this
work, we consider the formal context where the objects are the invoice images
and the attributes the predicates Ii = j, where Ii, i = 1, . . . , 5 denotes the five
textual information mentioned in Section 2.1, and j = 1, . . . , 43 denotes the
ID of the 43 prototype regions R1 ,...,R43 . An invoice on is in relation with a
predicate Ii = j if the textual information Ii is located at prototype region Rj
in the invoice on . A summary of this formal context is shown in Table 2.

3.2 Determination of paths from the concept lattice

Recall that, given a formal context K = (O, A, R), an association rule is a pair
(X, Y ), denoted as X → Y , where X and Y are disjoint subsets of A [1]. The set
X is called the antecedent of the rule X → Y and Y its consequent. The support
of an association rule X → Y is the proportion of objects that contain all the
)0 |
attributes in X ∪ Y , i.e. |(X∪Y
|O| . The confidence of X → Y is the proportion of
objects that contain Y , among those containing X. A (support,confidence)-valid
Table 2. Part of the Formal context of invoices data sets.

I1=10

I4=31
I4=32
I4=33
I5=34
I5=35
I5=36
I5=37
I5=38
I5=39
I5=40
I5=41
I5=42
I5=43
I1=1
I1=2
I1=3
I1=4
I1=5
I1=6
I1=7
I1=8
I1=9
...
o1 X ... X X
... ...
o895 X ... X X
... ...
o1000 X ... X X

association rule is an association rule whose support and confidence are at least
equal to a fixed minimum support threshold and a fixed minimum confidence
threshold, respectively. An approximate association rule is an association rule
whose confidence is less than 1. When the minimum support threshold is set to
0, the Luxenburger basis of approximate association rules is the set of rules of
the form X → Y \ X where X = X 00 , Y = Y 00 , X ⊂ Y and there is no Z such
that Z 00 = Z and X ⊂ Z ⊂ Y [21].
The Luxenburger basis can be visualized directly in the line diagram of a concept
lattice. Each approximate rule in the Luxenburger basis corresponds exactly to
one edge in the line diagram. The line diagram of a lattice contains paths by
which one can move from the top concept to the bottom one. The pathways
we adopt for browsing through the set of prototype regions are exactly those
corresponding to sequences of association rules of the Luxenburger basis, i.e.
top-down consecutive edges in the concept lattice. In other words, a pathway
is a sequence Y0 → Y1 → ... → Yn , where Y0 is the intent of the top formal
concept and for all 0 ≤ i < n, Yi → Yi+1 is an association association rule
of the Luxenburger basis. Given a node of the concept lattice, there are as
many approximate association rules of the Luxenburger basis whose antecedent
is the intent of this node, as are the children nodes of this node in the concept
lattice. Between two approximate association rules having the same antecedent,
the one with highest support is considered first. For instance, let a pathway
p1 : I5=42 → {I1=9, I3=25} holds with a support of 4% and a pathway p2 :
I5=42 → {I2=19, I3=28} holds with a support of 6%. In the aim to extract
information I1 to I5 from a candidate invoice image, and supposing that I5=42
is the lattice top node’s direct child node which holds the highest support value,
prototype region R42 is visited first in order to find information I5. Then, using
pathway p2 , prototype regions R19 and R28 are visited for finding information
I2 and I3 respectively. When, an information Ii is not found in a prototype
region given by pathway p2 , so p1 may be used to find it. Thus, all approximate
association rules given by the Luxenburger basis are used for information I1 to
I5 localization and extraction. In systems, such as, CREDO [8] and SearchSleuth
[12] the browsing strategy consists in focusing on a concept and its neighbors.
The effectiveness and performance for using this type of strategy in Web search
have been demonstrated in [8, 12].

4 Textual information extraction

To extract the textual information of interest, we perform an optical character
recognition (OCR) engine on prototype regions, using the pathways determined
in the previous step. Recall that a pathway is a sequence Y0 → Y1 → ... → Yn ,
where Y0 is the intent of the top formal concept and for all 0 ≤ i < n, Yi → Yi+1
is an approximate association rule of the Luxenburger basis. It should be noted
that each node Yα in such a sequence represents a set of predicates “Ii=j” in-
dicating that information Ii belongs to prototype region Rj . First, the set of
pathways is ordered by descending support value of the intents. Then, in each
node given by a pathway, an OCR engine is performed on each prototype region
in order to extract the corresponding information Ii. In formal language theory,
a regular expression is a sequence of characters that defines a search pattern,
mainly for use in pattern matching with strings. For each sought information Ii,
a regular expression is built and then used to check whether the extracted string
(by the OCR engine) matches with the given information.
In the literature, approaches such as in [34, 18, 19] are based on concept lattice
classifier and use a concept lattice for a classification task. Such approaches aim
to improve the task of character or symbol recognition in images. In [34], the
authors developed a recognition system named Navigala and fitted to recognize
noisy graphical objects and especially symbols images in technical documents
such as architectural plans or electrical diagrams. The authors noted that Navi-
gala is somewhat generic and can be successfully applied to other types of data.
In [18], the authors proposed modifications of some classifiers (naive Bayes, near-
est neighbor and random forest classifiers) in order to use the modified classifiers
as a part of the ABBYY OCR Technologies recognition schema for its perfor-
mance improving. The authors note that their approach based on random forest
can be applied to combine results of concept lattice classifiers.
In this paper, the task of text extraction is done with a free OCR engine named
Tesseract OCR (https://github.com/tesseract-ocr). Tesseract OCR was chosen
because it is a free software providing a JAVA API. In this paper, we focus
on the textual information localization task in administrative document images.
Indeed, OCR engine such as ABBYY OCR has a better recognition rate than
free OCR such as Tesseract OCR, but both are not able to localize or pick out a
given information such as the net amount in invoice images. Their task is just to
transform, as efficiently as possible, the text contained in images into plain text.
In this work, we propose to combine the proposed localization approach (based
on clustering analysis and navigation in a concept lattice) with any OCR engine
in order to extract a given information in document images without browsing
and recognizing the entire images.
5 Experimental results
We achieved an experiment in order to test the proposed model for textual
information extraction in real-world invoice images. The experiment consists
in extracting information I1 to I5, in the set of 1000 real-word invoice images
(Section2.1). Despite the fact that the approach was trained and tested with
good results on the synthetic data, in this section we present test results of the
approach on real-world invoice images. Indeed, the corpus of real-world invoice
images contains some noise which is not present in the synthetic invoice images.
The real-world invoice images may contain colored images such as a logo, shadow
areas and handwritten text. Additionally, they may be scanned with poor qual-
ity and may present distortion. Thus, the corpus of real-world invoice images
seems to us to be quite interesting for testing the proposed textual information
localization and extraction model. We performed two types of extraction:
1. from full images: OCR is performed on the entire page images regardless to
specific regions;
2. from prototype regions, using the pathways presented in Section 3: OCR is
performed only on image sub-regions, using the pathways.
To perform OCR on images, the JAVA library of the free OCR engine named
Tesseract in its 3.02 version is used. We considered two measures:
1. the rate of correct information among the total number of sought information
(recall),
2. the rate of correct information among the total number of detected informa-
tion (precision).
On the one hand, a sought information is considered detected, if a string which
matches the corresponding regular expression is found. On the other hand, a
sought information is considered correctly extracted, if the extracted textual
information corresponds exactly to the visual information that should be read
in the image. For instance, let ’net amount’ be a sought information and assume
that the net amount is 107e in the invoice image. During the process, if the
retrieved information is “101e”, the net amount will not be considered correctly
extracted because the real net amount mentioned in the original invoice image is
“107e”. The results are presented in Table 3. On the one hand, despite the fact
that according to [25], the Tesseract OCR engine has accuracy of 70% for text
extraction in gray scale number plate images, we observe that the accuracy of the
OCR engine is weak for text extraction in real-world invoice images. On the other
hand, these results show that our proposed model improves significantly the
performance of the OCR engine. Note that a p-value of 8.799e-05 was obtained
for this experiment, which means that the results are significant.

6 Conclusion and perspectives
We presented a (prototype) region-based model for localizing and extracting
textual information in document images. Experimental results show that the
Table 3. Results of experiments.

recall precision
OCR 29,84 % 37,36 %
OCR + pathways 35,88 % 50,46 %

proposed model improves significantly the correctness of a textual information
extraction process based on an OCR engine. This model is constructed in four
steps:
1. produce synthetic invoice data from real-world invoice images containing the
textual information of interest, along with their spatial positions;
2. partition the produced data;
3. derive the prototype regions from the obtained partition clusters;
4. derive pathways for browsing through the prototype regions, from the con-
cept lattice of a suitably defined formal context;
The Step 1 is important when one has to extract information from a previously
unseen invoice image. Indeed, if some information of such an invoice image are
not retrieved, then a synthetic representation of the considered invoice can be
produced, and this triggers incremental updates of the synthetic data sets, the
prototype regions, and the concept lattice. Our future work will focus on these
update processes, and compare them with those proposed in [3, 4, 9]. We also
plan to develop a classification model, as in [9], that will enable to predict the
invoice emitter based on the five textual information I1 to I5 considered in the
present paper. This will allow to easier retrieve the other textual information
the company is interested in: invoice number, invoice date, tax rate, tax due.

Acknowledgment
This work was partially carried out within the project ClustOverlap supported
by Reunion Island Region - grant DIRED 20140704. The authors are also very
grateful to the 3 anonymous reviewers for their valuable comments.

References
1. Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of
items in large databases. ACM SIGMOD Record, 22.2, pp. 207–216 (1993).
2. Barbut, M., Monjardet, B.: Ordre et classification: algèbre et combinatoire. Hachette
(1970).
3. Bartoli, A., Davanzo, G., Medvet, E., Sorio, E.: Semisupervised wrapper choice
and generation for print-oriented documents. IEEE Transactions on Knowledge and
Data Engineering 26.1, pp. 208–220 (2014).
4. Belaı̈d, A., D’Andecy, V. P., Hamza, H., Belaı̈d, Y.: Administrative document anal-
ysis and structure. Learning Structure and Schemas from Documents, pp. 51–71
(2011).
5. Birkhoff, G.: Lattice Theory. American Mathematical Society 25.3 (1967).
6. Cambria, E., White, B.: Jumping NLP Curves: A Review of Natural Language
Processing Research. IEEE Computational Intelligence Magazine 9.2, pp. 48–57
(2014).
7. Carpineto, C., Michini, C., Nicolussi, R.: A concept lattice-based kernel for SVM
text classification. Formal Concept Analysis, pp. 237–250 (2009).
8. Carpineto, C., Romano G.: Expoiting the Potential of Concept Lattices for Infor-
mation Retrieval with CREDO. Journal of Universal Computer Science, 10.8, pp.
985–1013 (2004).
9. Cesarini, F., Francesconi, E., Gori, M., Soda, G.: Analysis and understanding of
multi-class invoices. International Journal on Document Analysis and Recognition
6.2, pp. 102–114 (2003).
10. Charikar, M., Chekuri, C., Feder, T., Rajeev Motwani: Incremental clustering and
dynamic information retrieval. In: Proceedings of the twenty-ninth annual ACM
symposium on Theory of computing. ACM, pp. 626–635 (1997).
11. Cho, W.C., Richards, D.: Improvement of Precision and Recall for Information
Re- trieval in a Narrow Domain: Reuse of Concepts by Formal Concept Analy-
sis. In Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web
Intelligence (WI ’04), pp. 370–376 (2004).
12. Ducrou, J., Eklund, P. W.: SearchSleuth: The conceptual neighbourhood of an web
query. In J. Diatta, P. Eklund, & M. Liquire (Eds.), Proc. CLA 2007, LIRMM &
University of Montpellier II (2007).
13. Eisenbarth, T., Koschke, R., Simon, D.: Locating Features in Source Code. IEEE
Transactions on software engineering 29.3, pp. 210–224 (2003).
14. Ganter, B., Wille, R.: Contextual attribute logic. Springer Berlin Heidelberg
(1999).
15. Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations.
Springer Science & Business Media (2012).
16. Guyon, I., Cawley, G., Dror, G., Saffari, A.: Hands-on Pattern Recognition. Chal-
lenges in Machine Learning 1. Isabelle Guyon, Gavin Cawley, Gideon Dror, and
Amir Saffari editors (2011).
17. Hyontai, S.: An Effective Sampling Method for Decision Trees Considering Com-
prehensibility and Accuracy. WSEAS Transactions on Computers 8.4, pp. 631–640
(2009).
18. Itskovich, L., Kuznetsov, S.O.: Machine Learning Methods in Character Recogni-
tion. Proc. 13th International Conference on Rough Sets, Fuzzy Sets, Data Mining,
and Granular Computing (RSFDGrC 2011), Lecture Notes in Computer Science
6743, pp. 322–329 (2011).
19. Kaytoue, M., Kuznetsov, S.O., Napoli, A., Duplessis, S.: Mining gene expression
data with pattern structures in Formal Concept Analysis. Inf. Sci. 181(10): 1989-
2001 (2011).
20. Langley, R.: Practical statistics simply explained. Courier Corporation (1971).
21. Kuznetsov, S.O., Makhalova, T.P.: Concept interestingness measures: a compara-
tive study. CLA 2015, pp. 59–72 (2015).
22. Luxenburger, M.: Implications partielles dans un contexte. Mathématiques et Sci-
ences Humaines 113, pp. 35–55 (1991).
23. MacQueen, J. et al.: Some methods for classification and analysis of multivariate
observations. Proceedings of the fifth Berkeley symposium on mathematical statis-
tics and probability 1.14, pp. 281–297 (1967).
24. Murtagh, F., Legendre, P.: Wardś hierarchical agglomerative clustering method:
Which algorithms implement wardś criterion?. Journal of Classification 31.3, pp.
274–295 (2014).
25. Patel, C., Patel, A., Patel, D.: Optical character recognition by open source OCR
tool tesseract: A case study. International Journal of Computer Applications, 55.10,
pp. 50–56 (2012).
26. Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal Concept Analysis
in knowledge processing: A survey on applications. Expert systems with applications
40.16, pp. 6538–6560 (2013).
27. R Core Team: A language and environment for statistical computing. R Foundation
for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, (2013).
28. Singh, S.: Optical Character Recognition Techniques: A survey. International Jour-
nal of Advanced Research in Computer Engineering & Technology (IJARCET) 4.6,
pp. 545–550 (2013).
29. Sumathi, C.P., Santhanam, T., Devi, G.G.: A survey on various approaches of text
extraction in images 3.4, pp. 27–42 (2012).
30. Sumathi, C.P., Santhanam, T., Priya, N.: Techniques and challenges of automatic
text extraction in complex images: A survey. Journal of Theoretical and Applied
Information Technology, 35.2, pp. 225-235 (2012).
31. Taouil, R., Pasquier, N., Bastide, Y., Lakhal, L.: Mining bases for association rules
using closed sets. ICDE2000 International Conference, pp. 307 (2000).
32. van De Vel, M.L.J: Theory of convex structures. Elsevier (1993).
33. Vendramin, L., Campello, R., Hruschka, E.: Relative Clustering Validity Criteria:
A Comparative Overview. Statistical Analysis and Data Mining 3, pp. 209–235
(2010).
34. Visani, M., Bertet, K., Ogier, J.-M.: NAVIGALA: An original symbol classifier
based on navigation through a galois lattice. International Journal of Pattern Recog-
nition and Artificial Intelligence 25.4, pp. 449–473 (2011).
35. Wille, R.: Restructuring lattice theory: an approach based on hierarchies of con-
cepts. Springer Netherlands (1982).