Mathematical Formula Identification in PDF Documents

Conference Paper (PDF Available) · September 2011with303 Reads
DOI: 10.1109/ICDAR.2011.285 · Source: DBLP
Conference: 2011 International Conference on Document Analysis and Recognition, ICDAR 2011, Beijing, China, September 18-21, 2011
Abstract
Recognizing mathematical expressions in PDF documents is a new and important field in document analysis. It is quite different from extracting mathematical expressions in image-based documents. In this paper, we propose a novel method by combining rule-based and learning-based methods to detect both isolated and embedded mathematical expressions in PDF documents. Moreover, various features of formulas, including geometric layout, character and context content, are used to adapt to a wide range of formula types. Experimental results show satisfactory performance of the proposed method. Furthermore, the method has been successfully incorporated into a commercial software package for large-scale Chinese e-Book production.
Mathematical Formula Identification in PDF Documents
Xiaoyan Lin, Liangcai Gao
*
, Zhi Tang
Institute of Computer Science and Technology
Peking University
Beijing, China
{linxiaoyan, gaoliangcai,
tangzhi}@icst.pku.edu.cn
Xiaofan Lin
Vobile Inc
Santa Clara, CA, USA
xiaofan@vobileinc.com
Xuan Hu
College of Software
Beihang University
Beijing, China
huxuan@sse.buaa.edu.cn
Abstract—Recognizing mathematical expressions in PDF
documents is a new and important field in document analysis.
It is quite different from extracting mathematical expressions
in image-based documents. In this paper, we propose a novel
method by combining rule-based and learning-based methods
to detect both isolated and embedded mathematical
expressions in PDF documents. Moreover, various features of
formulas, including geometric layout, character and context
content, are used to adapt to a wide range of formula types.
Experimental results show satisfactory performance of the
proposed method. Furthermore, the method has been
successfully incorporated into a commercial software package
for large-scale Chinese e-Book production.
Keywords-mathematical expression recognition; formula
extraction; PDF document
I. INTRODUCTION
Nowadays an increasing number of documents are
available in the PDF format, which can greatly facilitate
document exchange and printing. Consequently research on
PDF document analysis is receiving more and more attention
[1] and significant progress has been made in recognizing
basic components of the PDF documents (e.g., headings,
paragraphs, table, etc) [2, 3]. However, as a crucial
component of the documents, mathematical formulas are still
recognized at accuracy too low to be useful in practical
applications. In order to make use of this valuable resource
in PDF documents, it is imperative to introduce better
mathematical expression recognition methods. Identifying
the regions of mathematical expressions is the first step in
this task.
An advantage of PDF document analysis is that the
character and layout information obtained from the PDF
parser is much richer and more accurate than that acquired
from OCR. In this sense, we can expect better results from
PDF document recognition.
However, there still remain some challenges when
extracting formulas from PDF documents. First, in the PDF
content stream, a mathematical expression element may be
composed of several different types of objects (e.g., text,
image, graph). Therefore, the content stream extracted from
the PDF document cannot be directly used as the logical
mathematical expression elements. For example, in a PDF
document generated by LaTeX, a root symbol is made up of
a graph object representing the horizontal line, and a text
object which is a radical character. The mismatch between
PDF objects and mathematical elements is one of the biggest
obstacles in formula extraction from PDF documents.
Second, PDF documents are usually generated by different
tools, and the objects used to render the mathematical
expressions vary significantly in the different PDF
generation programs. In addition, several PDF versions are
widely used at present and they have different internal
structures. To properly handle each type of PDF documents,
the task of matching PDF objects to mathematical expression
elements becomes even more difficult.
To overcome the above problems, Rahman et al. [2]
presented that a PDF document can be rendered to an image,
and then be analyzed by traditional recognition methods
designed for image documents. However, this method would
lose a lot of useful information which can be extracted
directly from PDF documents. In this paper, a preprocessing
step is proposed to focus on the above problems.
Baker et al. [4] proposed a formula recognition method
for PDF documents for the first time. However, they
assumed that the formula regions are already manually
clipped out before recognition. To our best knowledge, there
is no published work addressing how to identify formula
regions directly from PDF documents. In this paper, a
method is proposed to identify regions of both the isolated
and embedded mathematical expressions in PDF documents.
Preprocessing is first applied so that precise information of
PDF documents can be fully utilized. Then by using various
types of features (e.g., layout, characters, and context), we
combine both rule-based and learning-based methods to
adapt to a wide range formula types.
The rest of paper is organized as follows: Section II
reviews relevant work. Section III introduces our formula
identification method for PDF documents. Experimental
results are presented in Section IV. We conclude this paper
with a future research plan in Section V.
II. R
ELATED WORK
Traditional formula identification methods focus on
image-based documents. According to the types of features
used, the existing methods can be classified into three
categories: character-based, layout-based, and image-based.
The first category of methods [5-7] identifies the formula
*
Liangcai Gao is the corresponding author.
2011 International Conference on Document Analysis and Recognition
1520-5363/11 $26.00 © 2011 IEEE
DOI 10.1109/ICDAR.2011.285
1419
regions mainly through the character features (e.g., specific
math symbols or function names). These methods recognize
characters by OCR engine and the outliers from OCR are
considered as the candidates of mathematical expression
elements. In [5], characters not recognized as Japanese are
regarded as math symbols. Along this direction, Suzuki et al.
[6] added verification rules according to character positions
and sizes in order to develop a dedicated OCR system for
mathematics documents. Kacem et al. [7] constructed a
fuzzy logic model to identify math symbols, and then
utilized math symbols’ features (bounding box, relationship
between symbols, etc.) to merge or expand the character
regions to form the formula area. The shortcoming of
character-based methods is that they overemphasize
individual characters’ features without considering other
global features such as geometric layout. Besides, they
heavily depend on character recognition results of the OCR
system, in which recognition errors are inevitable.
The second category of methods [8-12] detects formula
areas through layout features (line heights, line spacing,
alignments, etc). For an isolated formula line, the line height
and spacing is larger than those of ordinary text lines, and it
is usually centrally aligned with a formula serial number at
the end of line. There are many variations of constructing the
quantitative models of layout features. In [8, 9], layout
features are used to build decision trees based on predefined
rules. These rule-based methods can only handle several
specific types of documents. In [10, 11], Garain built
quantitative models based on the statistics collected on a set
of documents. Several crucial thresholds are set based on the
statistics, which are very sensitive to the ratio of text lines
and formula lines. In [12], Jin et al. exploited a machine
learning technique (Parzen windows) to identify
mathematical formulas. However, the features utilized in
their method are limited, and thus it is not adaptive enough to
deal with the varieties of formula layout. In [13], another
learning-based method using computational geometry is
presented. Its classifiers are trained to distinguish
mathematics notations from English and may not be
applicable in documents in other language, such as Chinese.
The third category of methods extracts mathematical
expressions through image segmentation technique, without
using the character or layout features [14]. Although this
technique does not rely on the character recognition results,
the segmentation thresholds required by this technique are
hard to set, especially for documents of unknown types.
In summary, the existing formula extraction methods are
mostly designed for image-based documents, and various
types of features are not fully utilized and combined.
Consequently, there exists no robust method to identify
diverse types of formulas. Thus, we propose a method to
address this need.
III. P
ROPOSED METHOD
Fig. 1 shows the workflow of the proposed method. The
major steps include:
1. Preprocessing: Match the different types of objects
(text, image, and graph) to the mathematical expression
elements.
Figure 1. Workflow of the proposed formula extraction method
2. Text line detection: Text lines in the page are
extracted to be used as the basic units in the following steps.
3. Feature analysis: Character and layout features
representative of formulas are extracted.
4. Formula area detection: Rule-based method and
Support Vector Machines (SVM) classifier are used and
combined to identify the isolated formula areas. Rule-based
method is used to detect the embedded formula areas.
A. Preprocessing
The goal of preprocessing is to match the original
symbols parsed from a PDF document to the corresponding
mathematical expression elements. There are three types of
mathematical expression elements to be processed, including
mixed math symbols, mathematical functions, and numbers.
This step will output the descriptive details of the
mathematical expression elements, such as locations,
bounding boxes, baselines, fonts, which can be used as the
character or layout features in the following steps.
Preprocessing is critical because the better result it gets, the
richer and more precise information of PDF document can be
fully utilized later. Solutions are designed for different types
of mathematical expression elements:
Mixed math symbols: Some math symbols are
composed of different objects (graph, text, etc.) in the PDF
document content stream. For example, one mathematical
expression element may be made up of several graph objects
and/or text objects. Through observing the parsing results of
a large set of PDF documents, we find that mixed math
symbols can be classified into three categories according to
the composition of the mathematical expression elements: 1)
Some elements are composed of one text object and one
graph object. For example, a root symbol is composed of a
graph object representing the horizontal line, and a text
object which is a radical character. 2) Some elements are
composed of several graph objects. For example, a vertical
delimiter is made up of several vertical short line objects. 3)
One single graph object represents a mathematical
expression element. For example, fraction is represented as a
horizontal line graph object. Accordingly, we have
established a number of identification methods for each type
of the mixture symbols.
For instance, to extract a root symbol from the PDF
objects, we first locate all horizontal lines from the graph
objects. Then, we search the radical character by searching
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text objects whose Unicode is equal to ”. If so, the
graphic object and the text object, which are adjacent to each
other, are combined into a root symbol element.
PDF documents generated by different tools mainly
differ in the composition of the mixed math symbols. In this
paper, we take PDF documents of Version 1.3 generated by
LaTeX as an example to describe our approach. The
proposed method can work well on different versions of PDF
documents through replacing the matching strategies.
Named mathematical functions: We detect the named
mathematical functions by using a mathematical function
dictionary created from the official LaTeX documentations.
The sequence of characters representing named functions is
extracted and grouped into a mathematical expression
element tagged as a named mathematical function.
Numbers: Numbers can be detected among a string of
characters by regular expression, and the characters
representing a number are combined into a mathematical
element tagged as a number.
B. Text Line Detection
After the preprocessing step, detailed information of both
the candidate mathematical expression elements and the text
are available (bounding box, baseline, font and font size,
etc). Then text line detection is carried out. Reliable
identification of text lines benefits the detection of other
layout components, such as paragraphs and columns. For the
purpose of formula extraction, text lines can serve as a basic
unit of mathematical expressions. The isolated formula
extraction can then be simplified into distinguishing formula
lines from non-formula lines. In this paper we employ a
branch-and-bound text line finding algorithm proposed in
[15] to detect text lines .
C. Identification of Isolated Formulas
1) Feature Analysis
The features of isolated formulas can be classified into
three categories: geometric layout features, character features
and context features, whose definitions are listed in Table I.
The geometric layout features describe the text lines’
layout in a whole page and they are the most important
features for the isolated formulas. Generally speaking, the
geometric layout features are extracted from the result of text
line detection or character recognition, whose performance
varies with different typesetting styles or document quality.
Thus, it is a main bottleneck in processing image-based
documents. Fortunately, this is not a significant problem for
PDF documents, since the precise layout information of the
text lines and the characters are already obtained in the
preprocessing and text line detection steps.
Character features specify if certain mathematical
symbols or named functions exist in text lines. Character
features are simple but still effective. However, we have
noticed that these features have not been fully utilized in
previous work because a lot of math symbols cannot be
correctly recognized by OCR systems. Fortunately, here we
do not have to worry about this because almost all the math
symbols can be correctly parsed from the PDF document in
the preprocessing step discussed in Section III.A.
TABLE I. FEATURES OF THE FORMULAS
(“I” denotes isolated formula, and “E” denotes embedded formula.)
N
ame
Definition I E
Geometric layout features
AlignCenter
The relative distance of the line’s horizontal
center and the page body’ horizontal center.
V-Width. The variation between two lines’ widths.
V-Height A line’s height.
V-Space The space between two successive lines.
Sparse-Ratio
The ratio of the characters’ area of the line’s
area.
V-FontSize The variance of the font size.
SerialNumber
Whether there is a formula serial number in
the end of the line.
Italic Whether the character is in italic.
Character features
MathFunction
The named math functions (sin, cos, etc.),
defined in the math function dictionary.
√ √
MathSymbol
Math symbols are categorized into: binary
relations, binary operations, Greek letters,
delimiters, functions, integral, fraction, square.
√ √
Context features
Relationship
Whether the preceding/following character is a
formula element.
Domain
Describe operand domains of particular math
symbols such as the integral symbol.
√ √
Context features describe the relationship between
characters, based on the math symbols’ domain. Context
features are used to merge or to expand the characters’ areas
into a formula’s area.
2) Isolated Formula Detection
Since the isolated formulas have obvious layout features,
the layout features are used as the dominant features to detect
isolate formulas, and the character features are used as
auxiliary features. By utilizing these features, we adopt both
rule-based and learning-based methods to detect isolated
mathematical expressions:
Rule-based method: First, we use the character features
to filter out the lines which are very unlikely to be formula
lines. It is necessary to implement this step, for there are
some text lines (for example, title lines as noted in [11] as a
different corner case) sharing layout features with the
formula lines. Too strict rules may filter out true formula
lines and cause low recall rate. So currently the filtering rules
are very relaxed. A line is filtered out only when it does not
satisfy any of the following two rules: 1) A named function
appears in the line; 2) At least one math symbol appears in
the line. After the filtering step, title lines will be filtered out
because they usually contain neither math symbols nor
functions.
Second, the geometric layout features of isolated
expressions are exploited to calculate the confidence level of
classifying a line as a formula line. Geometric layout features
in Table I are utilized as binary features through comparing
the features with some thresholds, which are set through
statistical analysis. For example, if a line’s V-Height is larger
than the text lines’ average height of the page, the line has
this feature. We divide the importance of features into three
levels, based on the statistics collected on a large number of
PDF documents. Confidence scores are set according to the
levels. For example, the three levels correspond to 5, 2, and 1
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respectively. If a line has a feature, the corresponding score
is added to that line’s confidence score.
When the accumulated confidence level of a line is
higher than a threshold (vIF), the line is recognized as an
isolated formula. The value of the threshold is obtained by
statistical analysis on the data set.
Machine learning-based method: To decide if a line is
an isolated formula line is a classical binary classification
problem. Like many pattern recognition problems, the key
problem is to extract discriminating features. The geometric
layout and character features listed in Table I are employed
as a nine-element vector in our experiments. In our
implementation, LIBSVM, an optimized implementation of
Support Vector Machine (SVM) [16] is used to build the
SVM classifiers. Radial Basis Function (RBF) is employed
as the kernel function of SVM. The classifier is trained on
the labeled data to predict whether a line is an isolated
formula line.
Hybrid method: For each text line, the rule-based
method is first executed to calculate its confidence level as
formula line. If the confidence level is higher than vIF, it is
recognized as an isolated formula, otherwise, a SVM
classifier is employed to decide whether the line is an
isolated formula.
D. Identification of Embedded Formula
The goal of this step is to detect the areas of the
mathematical expressions in the text lines. The mathematical
expressions in line include equations, variables and
functions. There are very few layout differences between the
embedded expressions and the ordinary text. Therefore,
detecting embedded expressions relies mainly on character
features combined with supplementary layout features. A
rule-based method is adopted to detect embedded formulas.
1) Feature Analysis
Geometric layout features: In standard typesetting,
mathematical symbols are italic or bold to distinguish from
the ordinary text. This can be used as an important feature to
identify embedded formulas. However, under the influence
of the informal typesetting the font style information
sometimes is difficult to extract without heuristics, especially
in the image-based documents. Fortunately, for PDF
documents, font styles can be obtained in the PDF document
content stream. We exploit this distinctive feature to detect
the embedded formulas.
Character features: As the layout features of the
embedded mathematical expressions are limited, the most
significant features of the embedded formulas are the
character features. We divide the known math symbols into
eight categories (defined in the “MathSymbol” row of Table
I). Then these classes of symbols are used to look up the
dictionary during the detection process.
Context features: The context features in Table I, reflect
the relationships between characters and math symbol
domains, and they are used to merge or to expand the areas
of the math symbols in order to form the formula area.
2) Embedded Formula Detection
First, for each character or a sequence of characters in the
non-isolated formula lines, layout and character features are
used to calculate the likelihood of the character being a math
symbol. We divide importance of each class of math
symbols into different levels according to the uniqueness of
each type of symbols. Similar to the isolated formulas, the
confidence level is calculated according to the importance
level. A character is recognized as a mathematical expression
element when its accumulated confidence score is larger than
a threshold (vEF).
Second, for those characters tagged as math symbols, the
area of embedded expressions is obtained by merging areas
of math characters using context features defined in Table I.
IV. E
XPERIMENTAL RESULTS
To verify the effectiveness of the proposed method on
different types of formulas, we collect the data set from
mathematics textbooks written in English
1
and Chinese. In
total 421 pages are collected. Experiments are carried out on
200 randomly selected pages, which contain 5743 ordinary
text lines, 1541 isolated formulas and 3237 embedded
formulas.
For the hybrid method and learning-based method, the
data set is divided into five equal parts and five-fold cross-
validation is employed for training and testing. In our
experiments, the thresholds used to determine the area as
formula areas, vIF , vEF, are assigned values of 6 and 2,
respectively. An example of our formula extraction result is
shown in Fig. 2.
Figure 2. Example of the formula extraction
1
http://www.math.harvard.edu/~shlomo/
1422
A. Performance Evaluation
TABLE II. RESULTS OF THE ISOLATED FORMULA IDENTIFICATION
Method Precision Recall F1
Rule-based 90.54% 90.66% 90.60%
Learning-based 94.33% 97.01% 95.64%
Rule-based + Learning-based 94.45% 97.91% 96.14%
TABLE III. RESULTS OF THE EMBEDDED FORMULA IDENTIFICATION
Method
Precision Recall F1
R
ule-
b
ase
d
83.05% 84.18% 83.61%
We compare the performance of the hybrid method of
isolated formula identification with the rule-based method
and the learning-based method in Table II. Results of the
embedded formula identification is presented in Table III.
The evaluation metrics include three numbers: 1) Precision
is the probability that the extracted bounding boxes match
the formulas’ areas; 2) Recall is the probability that the
formulas are detected; 3) F1 is the harmonic mean of
precision and recall.
From the evaluation metrics, it is seen that the F1 of the
hybrid method is higher than that of rule-based and learning-
based methods by 5.54% and 0.50%, respectively.
The program is implemented in C++ and the tests are run
on a 2.50GHz PC with 2GB RAM. On average, it takes 10
seconds to detect formulas from 200 pages. For the hybrid
method and learning-based method, it takes less than 1
second to train the SVM classifier on a training set of 160
pages.
B. Analysis of the Experimental Results
The main cause of isolated formula identification errors
is that some short text lines containing math symbols are
recognized as isolated formulas. The precision and recall
rates of the embedded formula extraction are lower than
those of isolated formulas. There are a number of causes: 1)
Some embedded formulas are only partially recognized for
the deficiency of the merging and expanding rules. 2) Some
math symbols (e.g., ) in the text line cannot be recognized
by the PDF parser, therefore there are no character features
to distinguish them from ordinary text.
V. C
ONCLUSIONS
In this paper, a formula identification method targeting
PDF documents is introduced. It involves several steps:
preprocessing, isolated and embedded formulas extraction.
And the experimental results show satisfactory performance
of the proposed method. The contributions of this paper are
as follows: 1) Problems and difficulties of detecting
mathematical expressions in PDF documents are fully
analyzed, and an automated solution is provided. 2) Various
types of features, including character features, geometric
layout features, and context features, are deeply explored. In
addition, all of these features are combined with a carefully
defined weight scheme according to different types of
formulas. 3) The rule-based approach and learning-based
approach are combined to complement each other to improve
the performance.
In the future, we would apply our approach to process
different PDF documents produced by various tools and
improve the preprocessing procedure to adapt to different
versions of PDF documents. Another interesting research
direction is to automatically adapt the method's parameters
through machine learning.
VI. R
EFERENCES
[1] W.S. Lovegrove and D. F. Brailsford, “Document analysis of PDF
files: method, results and implications,” Electronic publishing, Sep.
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[2] F. Rahman and H. Alam, “Conversion of PDF documents into
HTML: a case study of document image analysis,” Conference
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Systems and Computers (ACSSC 03), Nov. 2003, pp. 87-91.
[3] H. Déjean and J.-L. Meunier, “A system for converting PDF
documents into structured XML format,” Proc. of Document Analysis
Systems (DAS 06), Jan. 2006, pp. 129-140.
[4] J. Baker, A. P. Sexton and V. Sorge, “A linear grammar approach to
mathematical formula recognition from PDF,” Proc. Springer Symp.
Intelligent Computer Mathematics (ICM 09), Jul. 2009, pp. 201-216.
[5] K. Inoue, R. Miyazaki and M. Suzuki, “Optical recognition of printed
mathematical documents,” Proc. of the third Asian Technology Asian
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[6] M. Suzuki, F. Tamari, R. Fukuda, S. Uchida and T. Kanahori, “Infty:
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printed mathematical formulas using fuzzy logic and propagation of
context,” IJDAR, vol. 4, no. 2, Dec. 2002, pp. 97-108.
[8] J.-Y. Toumit, S. Garcia-Salicetti and H. Emptoz, “A hierarchical and
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[9] S. P. Chowdhury, S. Mandal, A. K. Das and B. Chanda, “Automated
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[10] U. Garain and B. B. Chaudhuri, “A syntactic approach for processing
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[11] U. Garain, “Identification of mathematical expressions in document
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Analysis and Recognition (ICDAR 09), Jul. 2009, pp.1340-1344.
[12] J. Jin, X. Han and Q. Wang, “Mathematical formulas extraction,”
Proc. of International Conference on Document Analysis and
Recognition (ICDAR 03), Aug. 2003, pp. 1138-1141.
[13] D. M. Drake and H. S. Baird, “Distinguishing mathematics notation
from English text using computational geometry,” Proc. of
International Conference on Document Analysis and Recognition
(ICDAR 05), Aug. 2005, pp. 1270–1274.
[14] T.-Y. Chang, Y. Takiguchi and M. Okada, “Physical structure
segmentation with projection profile for mathematic formulae and
graphics in academic paper images,” Proc. of International
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Sep. 2007, pp. 392-396.
[15] M. B. Thomas, “High performance document layout analysis,” Proc.
Symp. on Document Image Understanding Technology (SDIUT 03),
Apr. 2003.
[16] C.-C. Chang and C.-J. Lin, LIBSVM: a library for support vector
machines, 2001, http://www.csie.ntu.edu.tw/~cjlin/libsvm.
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    • "Based on the proposed dataset and metric, we evaluate the mathematical formula identification methods presented in our previous work [11], including rule-based, SVM-based, and hybrid methods. Evaluation on other existing formula identification methods is not conducted in this paper due to: a) The source code of the reported mathematical formula identification methods is unavailable; b) Published description is not always sufficient for implementation; c) As far as we know, the only accessible math formula recognition tool is InftyReader provided by Infty [1]. "
    [Show abstract] [Hide abstract] ABSTRACT: This paper presents a performance evaluation system for mathematical formula identification. First, a ground-truth dataset is constructed to facilitate the performance comparison of different mathematical formula identification algorithms. Statistics analysis of the dataset shows the diversities of the dataset to reflect the real-world documents. Second, a performance evaluation metric for mathematical formula identification is proposed, including the error type definitions and the scenario-adjustable scoring. The proposed metric enables in-depth analysis of mathematical formula identification systems in different scenarios. Finally, based on the proposed evaluation metric, a tool is developed to automatically evaluate mathematical formula identification results. It is worth noting that the ground-truth dataset and the evaluation tool are freely available for academic purpose.
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