Available via license: CC BY 4.0
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Submitted 22 July 2020
Accepted 23 December 2020
Published 15 February 2021
Corresponding author
Renier Mendoza,
rmendoza@math.upd.edu.ph
Academic editor
Arkaitz Zubiaga
Additional Information and
Declarations can be found on
page 20
DOI 10.7717/peerj-cs.360
Copyright
2021 Pino et al.
Distributed under
Creative Commons CC-BY 4.0
OPEN ACCESS
Optical character recognition system for
Baybayin scripts using support vector
machine
Rodney Pino, Renier Mendoza and Rachelle Sambayan
Institute of Mathematics, University of the Philippines Diliman, Quezon City, Metro Manila, Philippines
ABSTRACT
In 2018, the Philippine Congress signed House Bill 1022 declaring the Baybayin script
as the Philippines’ national writing system. In this regard, it is highly probable that
the Baybayin and Latin scripts would appear in a single document. In this work,
we propose a system that discriminates the characters of both scripts. The proposed
system considers the normalization of an individual character to identify if it belongs
to Baybayin or Latin script and further classify them as to what unit they represent.
This gives us four classification problems, namely: (1) Baybayin and Latin script
recognition, (2) Baybayin character classification, (3) Latin character classification,
and (4) Baybayin diacritical marks classification. To the best of our knowledge, this is
the first study that makes use of Support Vector Machine (SVM) for Baybayin script
recognition. This work also provides a new dataset for Baybayin, its diacritics, and
Latin characters. Classification problems (1) and (4) use binary SVM while (2) and (3)
apply the multiclass SVM classification. On average, our numerical experiments yield
satisfactory results: (1) has 98.5% accuracy, 98.5% precision, 98.49% recall, and 98.5%
F1 Score; (2) has 96.51% accuracy, 95.62% precision, 95.61% recall, and 95.62% F1
Score; (3) has 95.8% accuracy, 95.85% precision, 95.8% recall, and 95.83% F1 Score;
and (4) has 100% accuracy, 100% precision, 100% recall, and 100% F1 Score.
Subjects Artificial Intelligence, Computer Vision, Natural Language and Speech, Optimization
Theory and Computation, Scientific Computing and Simulation
Keywords Baybayin, Latin script identification, Baybayin script identification, Support vector
machine, Optical character recognition
INTRODUCTION
Baybayin is one of the pre-Hispanic writing systems used in the Philippines (Cabuay,
2009). In 2018, the Philippine government is showing efforts to preserve and reintroduce
this heritage, mandating all local government units to inscribe Baybayin script with
its translation in their communication systems (e.g., signage), through House Bill 1022.
Furthermore, local manufacturers are required to use Baybayin on labels, and the Education
Department is tasked to promote the said writing system (Lim & Manipon, 2019).
Baybayin is an abugida, or alphasyllabary, primarily used by the Tagalogs in northern
Philippines during the pre-colonial period. Baybayin consists of 17 unique characters:
14 (syllabic) consonants and three vowels (see Fig. 1A). In Baybayin, consonants are
pronounced with an inherent vowel sound ‘ \a\’, and diacritics are used to express the
other vowels. For example, a diacritic written above a consonant character may represent
How to cite this article Pino R, Mendoza R, Sambayan R. 2021. Optical character recognition system for Baybayin scripts using support
vector machine. PeerJ Comput. Sci. 7:e360 http://doi.org/10.7717/peerj-cs.360
Figure 1 (A) Baybay in characters (without diacritics) with equivalent Latin syllable; (B) Latin alpha-
bet in upper- and lowercase.
Full-size DOI: 10.7717/peerjcs.360/fig-1
an accompaniment vowel ‘ \e\’ or ‘ \i\’, while a diacritic written below may represent an
‘\o\’ or ‘ \u\’ sound. Diacritics can also be used to silence the vowel sounds. Figure 2
shows an example of the phonetic distinction in a Baybayin consonant character using
diacritical marks. While there are no standard accent symbols for Baybayin script, the most
commonly used are bar, dot, cross, or x. A bar or a dot represents the vowels E/I or O/U
based on their placement, while the cross or x symbol located below the character cancels
the vowel ‘‘a’’ (Cabuay, 2009).
Optical Character Recognition (OCR) is a process of reading and recognizing
handwritten or printed characters. It belongs to the family of machine recognition
techniques where the system performs an automatic identification of scripts and characters
(Chaudhuri et al., 2017).
Prior to choosing an appropriate character recognition algorithm, it is important to
determine first the script used in writing a document. Most research studies in script
identification have considered printed and handwritten scripts, and are based on several
levels: page, line, word, and character level (Ghosh, Dube & Shivaprasad, 2010). With
English as a global language and Latin as its standard script, there have been a lot of
script identification techniques that discriminate the Latin script from other writing
systems. Jaeger, Ma & Doermann (2005) have identified the Latin script from Arabic,
Chinese, Hindi, and Korean scripts using modified document spectrum algorithm and
four classifier systems at word level. At text block level script identification, Zhou, Lu &
Tan (2006) have distinguished Bangla against Latin script using connected component
analysis. Several studies by Chanda, Pal & Kimura (2007) about script recognition include:
identifying Japanese from Latin script using a single text line scheme based on tree classifier,
recognizing Thai from Latin script using a single text line scheme based on Support Vector
Machine (SVM) (Chanda, Terrades & Pal, 2007), and recognizing Latin from all Han-based
scripts (Chinese, Japanese, and Korean) via a multi-script OCR system that uses chain-code
histogram-based directional features and SVM at character and block level (Chanda et al.,
Pino et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.360 2/24
Figure 2 Incorporating diacritics in a Baybayin (consonant) character. The dot diacritic changes the
‘Ba’ character in A into ‘Be’ or ‘Bi’ character if written above the character (B), and into ‘Bo’ or ‘Bu’ if
written below the character (C). A cross diacritic written below the ‘Ba’ character cancels the vowel ‘a’ (D).
Full-size DOI: 10.7717/peerjcs.360/fig-2
2010). At character level, Zhu et al. (2009) have reported recognition of Chinese script
from Latin script using feature selection and cascade classifier, while Rani, Dhir & Lehal
(2013) have proposed a technique using Gabor filter and gradient-based features using
SVM classifier to recognize Gurumukhi and Latin scripts. At word level, on the other hand,
Hangarge, Santosh & Pardeshi (2013) have reported script recognition based on directional
discrete cosine transforms (D-DCT) to six Indic scripts, namely, Devanagari, Kannada,
Telugu, Tamil, Malayalam, and Latin. Rajput & Ummapure (2017) have employed Scale
Invariant Feature Transportation (SIFT) approach and K−Nearest Neighbor (KNN)
classifier to identify Latin, Kannada, and Devanagari scripts. Multi-script identification
at page level based on features extracted via Structural and Visual Appearance (SVA)
and Directional Stroke Identification (DSI) has been proposed by Obaidullah et al. (2017).
Recognition of eleven Indian scripts together with the Latin script has been carried out using
Multilayer Perceptron (MLP) and Simple Logistic (SL) classifiers. Script identification for
Chinese, Kannada, Greek, Latin, and other scripts in natural scene text images and video
scripts has been done by extracting global and local features, with the use of attention-based
Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) Network
(Bhunia, Konwer & Bhunia, 2019).
Among the many varieties of the OCR algorithm, the SVM classifier is one of the most
popular because of its high response speed, robustness, and good accuracy (Thomé, 2012).
The SVM algorithm was first introduced by Vladimir Vapnik and Alexey Chervonenkis
in 1963. It belongs to the family of supervised classification techniques based on statistical
learning theory. SVM algorithm has been very well-developed for the past decade where it
Pino et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.360 3/24
seeks the optimal hyperplane that maximizes the margins between the borders of two classes
(Thomé, 2012). For an extensive discussion on how SVM works, we refer the readers to
Cristianini & Shawe-Taylor (2000);Shawe-Taylor & Cristianini (2004);Bishop (2006), and
Schölkopf & Smola (2012). Several applications of SVM include face and object detection
and recognition, image information retrieval, time series prediction (Sapankevych &
Sankar, 2009), speech recognition (Ganapathiraju, Hamaker & Picone, 2004), data mining
(Nayak, Naik & Behera, 2015), bioinformatics (Yang, 2004;Rivero, Lemence & Kato, 2017;
Rivero & Kato, 2018;Do & Le, 2019), and genomics (Le et al., 2019;Le, 2019). Among these
many applications, SVM is reported to greatly outperform most of the other learning
algorithms for handwritten character and digit recognition problems (Byun & Lee, 2003).
Many script character recognition systems have been reported with SVM as classifiers. In
their experiments, Phangtriastu, Harefa & Tanoto (2017) have shown that SVM can achieve
an accuracy of 94.43%, and is better when compared to Artificial Neural Network. Tautu
& Leon (2012) have studied how SVM can be used to classify handwritten Latin letters, and
obtained over 90% precision when tests were implemented on small or capital Latin letters.
Sok & Taing (2014) have proposed SVM for printed Khmer Characters and obtained
98.62% recognition accuracy. Using SVM models, Shanthi & Duraiswamy (2009) have
reported recognition for Tamil characters and obtained 82.04% accuracy. With 96.79%
recognition rate, identification of Arabic handwritten characters have been presented by
Althobaiti & Lu (2018) with the use of SVM classifiers and Normalized Central Moments
(NCM) and Local Binary Patterns (LBP) for feature extraction. Aggarwal & Singh (2015)
have reported gradient and curvature approach in feature extraction and have made use
of SVM models in recognizing Gurmukhi characters where they obtained an accuracy
of 98.56%. Using Zernike invariants and SVM classifiers, Kaushal, Khan & Varma (2014)
have proposed a technique in identifying Urdu characters and obtained a recognition
accuracy of 96.29%. Dong, Krzyak & Suen (2005) have used directional histograms and
SVM models to recognize Chinese characters. Their work yields a 99% recognition rate. An
accuracy of 87.32% have been reported by Kilic et al. (2008) using SVM model for Ottoman
character recognition. Recognition of Malayalam characters using wavelet transforms and
SVM classifiers have been introduced by John, Pramod & Balakrishnan (2012), where
their empirical results obtained a 90.25% accuracy. Gaur & Yadav (2015) have performed
K-means clustering and used SVM classifiers to recognize Hindi characters and obtained
a result of 95.86% accuracy. Utilizing SVM and combining Zoning and Gabor filter in
feature extraction yields a result of 92.99% recognition rate in classifying Bangla characters
(Pervin, Afroge & Huq, 2017).
Baybayin OCR is still in its infancy in literature. There are only few mathematical studies
on Baybayin script. Recario et al. (2011) have presented an automated reader for Baybayin
scripts where the system outputs the equivalent syllables of the Baybayin character. They
used the Line Angle Categorization and Freeman Chain Coding for classification, and
obtained results of 51.96% and 66.47% recognition accuracy, respectively. Nogra, Romana
& Maravillas (2019) and Nogra, Romana & Balakrishnan (2020) have proposed an LSTM
neural network and CNN models, with 92.90% and 94.00% recognition rates, respectively,
that translates Baybayin handwritten characters to their corresponding syllabic unit in Latin
Pino et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.360 4/24
alphabet. While their works were done at character level, they have greatly contributed
in introducing Baybayin characters to computer vision. Recio & Mendoza (2019) have
implemented an edge detection approach on recognizing old images containing Baybayin
texts. The method has been generally shown to be effective in detecting the texts’ edges and
reducing the noise level of an old image.
In line with the restoration of the script, we propose an OCR system that distinguishes
Baybayin from the Latin script at character level in either handwritten or computer-
generated form. The 17 main Baybayin characters and 52 (26 each for upper and lower
cases) Latin characters are shown in Fig. 1. In this paper, we introduce a technique in
classifying the characters of both scripts with the aid of SVM.
The remainder of this paper is organized as follows: in ‘A Review of Support Vector
Machine’, a brief review of how support vector machine works is presented. ‘Collecting
Baybayin Dataset’ discusses how Baybayin, its accents, and Latin characters were gathered.
The proposed OCR algorithm for Baybayin and Latin script and character recognition are
presented in ‘Proposed OCR System’. In ‘Experimental Setup, Results and Discussions’,
the results and discussion of our proposed OCR algorithm are shown. Lastly, conclusions
and future works are presented in ‘Conclusions and Future Works’.
A REVIEW OF SUPPORT VECTOR MACHINE
Our proposed OCR system consists of four classification problems wherein SVM is used.
SVM considers a training set of points Exi∈Rn,i=1 ,. .. ,N, where nis the number of
features in a particular training sample and Nis the number of training points. In a
two-class or binary classification problem, each of these points are labeled by an indicator
variable yi∈ {−1,1}, depending on the class in which the data point belongs. The points
are separated by classes or categories by a hyperplane called a linear classifier. The linear
classifier can be written with a set of points Exsatisfying
Ew· Ex+b=0,(1)
where Ewand bare the weight vector and bias term, respectively. With vector Ewnormal to
the hyperplane (Eq. (1)), the distance of the hyperplane from the origin is |b|/k Ewk.
In a linearly separable case (i.e., it is possible to draw a line that can separate the two
classes), we can select two parallel hyperplanes (dashed lines in Fig. 3) that separate the
two classes with maximum distance from each other, in a way that there are no data points
in between. The linear classifier (Eq. (1)) lies halfway between these hyperplanes, and the
region bounded by one of these hyperplanes and the linear classifier is called a margin.
SVM finds the values for the weight and bias that gives the maximum-margin hyperplane
that separates the two classes, so that any point on or above the hyperplane
Ew· Ex+b=1
is labeled 1 while points on or below
Ew· Ex+b= −1
Pino et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.360 5/24
Figure 3 The maximum-margin hyperplane. The figure shows an example of a two-class data separated
by a linear classifier. The parallel dashed lines represent the hyperplanes Ew· Ex+b=1 and Ew· Ex+b=
−1 where the distance between them is 2
k Ewk. The circular-shaped data belong to the positive group (labeled
with y=1) while the triangular-shaped data belong to the negative group (labeled with y= −1). The
points on the dashed lines are called support vectors. SVM finds the optimal hyperplane (solid line) whose
distance from the origin is b
k Ewkand lies halfway between the two (dashed) hyperplanes.
Full-size DOI: 10.7717/peerjcs.360/fig-3
is labeled −1. Hence, for any training data point Exiand its corresponding label yi, we have
yi(Ew· Exi+b)≥1,i=1,.. ., N,
and the optimization problem is then given by
minimize
Ew,b
1
2k Ewk2
subject to yi(Ew· Exi+b)≥1 for all i=1,...,N.(1)
The primal Lagrangian is constructed as
L(Ew,b,Eα)=1
2k Ewk2−
N
X
i=1
αiyi(Ew· Exi+b)−1,(2)
where α1,α2, .. ., αN≥0 are Lagrange multipliers. Differentiating (Eq. (3)) with respect to
Ewand band then equating to zero, yields
Ew=
N
X
i=1
yiαiExi(3)
and
N
X
i=1
αiyi=0,(4)
Pino et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.360 6/24
respectively. Substituting (Eqs. (4)) and ((5)) to ((3)), the primal Lagrangian (Eq. (3))
becomes
L(Ew,b,Eα)=
N
X
i=1
αi−1
2
N
X
i=1
N
X
j=1
yiyjαiαj(Exi· Exj).(5)
The Karush–Kuhn–Tucker (KKT) conditions assert that the optimal solutions Eα∗,Ew∗,
and b∗must satisfy
α∗
iyiEw∗· Exi+b∗−1=0,(6)
which implies that for all nonzero Eα∗
i,
yiEw∗· Exi+b∗=1,
where the Exi’s are precisely the data points or vectors on the margin. These vectors are
called support vectors as they are the ones which determine or ‘‘support’’ the margins. From
(Eqs. (1)) and ((4)), the maximum-margin hyperplane is now given by
X
i∈S
yiα∗
i(Exi· Ex)+b∗=0,(7)
where Sis the set of indices of the support vectors. A new data point Ex∈Rncan now be
assigned a class through the decision function
f(Ex)=sign X
i∈S
yiα∗
i(Exi· Ex)+b∗!,(8)
using only the support vectors.
Although there are datasets that can be linearly classified, this is not usually the case,
as can be seen in Fig. 4A. As a solution, Boser, Guyon & Vapnik (1992) proposed that the
data in the input space be mapped into a higher-dimensional space, called a feature space,
where a linear separator could be found (see Fig. 4B). The computation of the optimal
hyperplane in this scenario is not done explicitly on the feature space but rather by the use
of a kernel trick. This is briefly discussed below.
Suppose that the data points in the input space are mapped into a feature space by using
some nonlinear function φ. Then (Eq. (6)) becomes
L=
N
X
i=1
αi−1
2
N
X
i=1
N
X
j=1
yiyjαiαjφ(Exi)·φ(Exj).(9)
Similarly, (Eqs. (8)) and ((9)) become
X
i∈S
yiα∗
iφ(Exi)·φ(Exj)+b∗=0,(10)
and
f(Ex)=sign X
i∈S
yiα∗
iφ(Exi)·φ(Exj)+b∗!,(11)
Pino et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.360 7/24
Figure 4 Mapping of the data points from the input space onto a feature space. (A) One-dimensional
dataset that cannot be separated by a linear classifier. (B) The data points in A are mapped into a higher-
dimensional space (feature space) where a linear classifier is found.
Full-size DOI: 10.7717/peerjcs.360/fig-4
respectively. Notice that in the above equations, the inner product happens only on the
images of the inputs Exiand Exj. Mercer’s theorem states that if,
κExi,Exj=φ(Exi)·φ(Exj) (12)
is positive-definite, then the function κin (Eq. (13)) is called a kernel function.Equation
(13) also tells us that as long as the function κis positive-definite, we are assured of some
inner products of the images in the feature space. This follows that the coordinates of the
images or mapped data points need not be explicitly computed, and the kernel trick allows
us to write (Eqs. (10)), ((11)), and ((12)) as
L=
N
X
i=1
αi−1
2
N
X
i=1
N
X
j=1
yiyjαiαjκExi,Exj,(13)
X
i∈S
yiα∗
iκExi,Exj+b∗=0,and (14)
f(Ex)=sign X
i∈S
yiα∗
iκExi,Exj+b∗!,(15)
respectively. Some commonly-used kernels are as follows:
•Linear kernel: κ(Eu,Ev)= Eu· Ev.
Pino et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.360 8/24
•Polynomial kernel: κ(Eu,Ev)=(Eu· Ev+r)n, where rand nare the polynomial coefficient
and degree, respectively.
•Radial Basis Function (RBF) kernel: κ(Eu,Ev)=e−kEu−Evk2
2σ2, where σis an arbitrary constant.
Among these three kernel functions, the RBF kernel is the most versatile and preferred,
especially when not much of the data is known (Sok & Taing, 2014). The numerical results
in Tautu & Leon (2012) showed that RBF fared well compared to other kernels in classifying
handwritten characters.
COLLECTING BAYBAYIN DATASET
Data preparation is the main part of the system. In this section, we discuss how all
the necessary dataset for Baybayin, its accents, and Latin characters were collected and
compiled. In implementing essential functions to generate the dataset, MATLAB (vR2018a)
Image Processing Toolbox was used.
For uniformity of dataset, each image was converted into binary data using a modified
k-means clustering. K-means clustering provides a simple and flexible technique in
grouping image intensities. It gives a good performance even in dark images and operates
at a low computational complexity since it reduces the data dimension (Pourmohammad,
Soosahabi & Maida, 2013). It chooses kcenters so as to minimize the total squared distance
between each point and its closest center. The number of centroids is equal to the number
of clusters.
In this study, k=2 was used in the modified k-means function for image binarization.
The input image was clustered into two intensities, where lower intensities were set to 1s
(white pixels), while higher intensities were set to 0s (black pixels). The MATLAB function
regionprops was implemented to provide measurements for each connected component
in the input binary image, and the following output were utilized: area, bounding box, and
centroid. The image associated with the bounding box was then cropped using the imcrop
function to limit the data into its significant features. The cropped image was then rescaled
to 56 ×56 pixels using imresize function. To denoise the resized image, the command
bwareaopen was used. This command removes all regions that have less pixels than the set
pixel value. Finally, the image’s feature vector was generated and then compiled for SVM
training. Algorithm 1 and Fig. 5 illustrate the data preparation process succinctly.
For our dataset collection, the images of each Baybayin and Latin characters were
searched online. A total number of 9,000+ images for Baybayin characters were taken
from the dataset provided by Nogra (2019) in Kaggle. However, some characters in this
dataset have less number of images than the other characters. Thus, to complete the dataset,
additional images from various websites and books were collected through the use of a
snipping tool. Computer-generated and (noisy) handwritten images were also added, as
long as the character and its features are visible. The number of images per character was
set to 1,000, and after binarization, Algorithm 1 was implemented to each of the binarized
images.
Meanwhile, the images for Latin characters were taken from Vial (2017) and Nano
(2016) in Kaggle. The generated Latin dataset was set to 700 images per character, with a
Pino et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.360 9/24
Figure 5 Feature extraction process of a raw character image. (A) Raw character image, 106 ×108; Bi-
nary image of A, 106 ×108. (C) Character bounding box, 106 ×108. (D) Cropped image, 86×99. (E) Re-
sized image, 56×56. (F) Denoised image, 56×56.
Full-size DOI: 10.7717/peerjcs.360/fig-5
Algorithm 1 Feature Extraction Process
Require: Character binary image.
Ensure: Feature vector of size 3,136 with elements 0s and 1s.
1: Identify the character’s significant features by computing its bounding box measure-
ment.
2: Crop the image associated with the bounding box measurement to fit and obtain the
character’s essential area.
3: Rescale the cropped image into a size of 56×56 pixels.
4: Denoise unnecessary region/components that are not connected to the character.
5: Extract its feature vector by concatenating each row right next to each other to form a
1×3,136 array.
balanced number of upper and lower cases. The binarized images were also preprocessed
using Algorithm 1.
Baybayin diacritic images were also gathered for classification training. Images for dots,
bars, cross, and x were collected from Nano (2016) in Kaggle. The images for minus, plus,
and times symbols, together with the letter ‘‘o’’, were modified to produce the diacritic
symbols. The binarized images also underwent Algorithm 1 for preprocessing, but due
to the characters’ thinness, the imdilate function was applied after the bwareaopen
Pino et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.360 10/24
operation to dilate the binary input and provide a preferable data than the original image.
A total of 500 images for each diacritic was produced.
The generated dataset for the Baybayin and Latin characters and Baybayin diacritics can
be accessed in Pino (2020a).
PROPOSED OCR SYSTEM
The proposed system assumes that the character input has the following properties:
•The character print has the lowest intensity (darkest) than any other part of the image.
•If an input is a Baybayin character with a diacritic or accent, the main body (character)
has the largest number of pixels in the image, followed by the accent.
•The accent symbol should be written properly (i.e., the accent symbol should be above
or below the main character, should not be touching the main character, and must be
within the width of the main character).
From an input character image, the system starts with a preprocessing part where the
original image is transformed into binary data. If the input has only one component (see
Fig. 2A), the system proceeds as in Algorithm 1. On the other hand, if the input has an accent
(Figs. 2B to 2D), we locate and separate the main component from the accent/diacritic
component, then both components are preprocessed using Algorithm 1. With this, the
rest of the structure of the system is constructed from location segmentation, component
normalization, feature extraction, classification, and character recognition. Figure 6 shows
the overall flow of the system.
Location segmentation and feature extraction
This is trivial for the one-component image. For the two-component image, after the
binarization of the original image using modified k-means, the function regionprops
was implemented to obtain the information on the image components. Then, the imcrop
function was applied to extract the two highest area components (main body and accent).
The two components were then isolated. Steps 3 –5 of Algorithm 1 were then implemented
to the (isolated) components to obtain their respective 1×3,136 feature vectors, which will
later be used for classification.
Character classification and recognition
For classification and recognition processes, four main SVM classifiers were considered.
The main body feature vector Xgoes through two classification nodes as shown in Fig. 7.
The first classifier discriminates the Baybayin from the Latin script. Afterwards, if the first
classifier determines Xto be in Baybayin alphabet, Xproceeds to the Baybayin character
classifier and identifies which character unit it represents. A similar method applies if Xis
recognized as a Latin character by the first classifier node. In the case where there is more
than one component, the system assumes that Xis a Baybayin accent (see Fig. 6), and its
feature vector goes to the Baybayin diacritic SVM classifier.
Pino et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.360 11/24
Figure 7 Main body classification process using SVM classifiers.
Full-size DOI: 10.7717/peerjcs.360/fig-7
One component case
From the result obtained in the main body classification, if the image is classified as a
Baybayin character, the system prints out its Latin equivalent. On the other hand, if the
image is classified as Latin, the Latin character itself is printed out.
Two components case
In the two components case, the system separates the main body of the character image
from its diacritic (or accent), and identifies the main body as either Baybayin or Latin
character.
If the main body is classified as a Baybayin character, the diacritic classifier result is
combined with the Baybayin character for syllable classification. The system determines
the correct placement of the accent through its ordinate values from the centroid’s
coordinates. That is, if the accent’s centroid ordinate value is less than the main body’s,
then its placement is above the character (see Fig. 8). On the other hand, if the accent’s
centroid ordinate value is greater than the main body’s, then its placement is below
the character. The Baybayin diacritic classifier then discriminates between the dot-bar and
Pino et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.360 13/24
Figure 8 Binary image with the centroid locations superimposed.
Full-size DOI: 10.7717/peerjcs.360/fig-8
cross-x symbol. Finally, the system prints out the Latin equivalent of the Baybayin character
with the recognized associated unit represented by the accent.
Meanwhile, if the main body is classified as a Latin character, then there is a chance that
a noise was mistaken as another component or it could be that the character is either an
‘i’ or a ‘j’. In the latter case, the system ignores the accent classifier and prints directly the
identified Latin character.
OCR system main process
This section summarizes the main process of the proposed system. Given an individual
Baybayin or Latin character input image, the system converts the input into a binary image.
Afterwards, if the system detects only one region/component from the input, it will proceed
directly to Algorithm 1 for feature extraction process which serves as input for the main
body classification. The main body classification process contains SVM classifiers that will
categorize the input vector as either Baybayin or Latin, and further recognize it as to what
character it represents. The system then outputs the corresponding default Latin syllable
for Baybayin characters and an equivalent Latin letter for Latin characters.
For the two component case, the system assumes that the other region represents the
Baybayin diacritic symbol. After converting the raw input image into binary data, the
system locates the two components’ centroids and computes for their respective region of
interest (bounding box). Each of these components then goes through a similar process as
in Algorithm 1. The resulting feature vector for the main body proceeds to the main body
classification while the accent’s feature vector will be fed into the Baybayin diacritic SVM
classifier. If the main body classification results to a Latin script, the system ignores the
Baybayin diacritic classifier result and outputs directly its equivalent Latin letter. Otherwise,
the Baybayin character is combined with the diacritic information, the placement of which
is based on the centroid and the diacritic classifier result. The system then outputs the
equivalent Latin syllable or unit it represents. Algorithm 2 summarizes the overall OCR
process. The MATLAB code used in this work can be accessed in GitHub shared by Pino
(2020b).
Pino et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.360 14/24
Algorithm 2 Proposed OCR System.
Require: Isolated Baybayin or Latin character image.
Ensure: Equivalent Latin syllable/unit.
1: Convert the input image into binary data.
2: Count the number of components Nfrom the binary image.
3: if N=1then
4: Implement Algorithm1.
5: Feed the feature vector Xinto character script classifier.
6: if Baybayin then
7: Feed Xinto Baybayin character classifier.
8: else
9: Feed Xinto Latin character classifier.
10: end if
11: Print the identified script and its default equivalent Latin unit.
12: else
13: Locate the centroids of two components with highest pixel area.
14: Identify the main body and the accent components.
15: Implement Algorithm1 to both selected components.
16: Feed the accent feature vector into Baybayin diacritic classifier.
17: Feed the main body feature vector Xinto character script classifier
18: if Latin then
19: Discard Baybayin diacritic classifier result.
20: Feed Xinto Latin character classifier.
21: else
22: Feed Xinto Baybayin character classifier.
23: Combine the result with the Baybayin diacritic classifier outcome.
24: if Accent is located above then
25: E/I vowel concatenation.
26: else
27: if Bar or dot then
28: O/U vowel concatenation.
29: else
30: Vowel cancellation.
31: end if
32: end if
33: end if
34: Print the identified script and its corresponding Latin unit.
35: end if
Pino et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.360 15/24
Table 1 Templates used in MATLAB SVM training.
Property Name/Set to Setup Description
Kernel Function/‘rbf’ The software makes use of Gaussian kernels in implementing the algo-
rithm to generate a classification model.
Data Standardization/‘true’ The software standardize the predictors before training the classifier for
a potential decrease in classification error.
Box Constraint Parameter/‘inf’ The software makes a strict classification which means there will be no
points misclassified in training.
Kernel Scale Parameter/‘auto’ The software applies an appropriate kernel norm to compute the Gram
matrix that arises from kernel functions.
EXPERIMENTAL SETUP, RESULTS AND DISCUSSIONS
The SVM models presented here were obtained using the MATLAB (vR2018a) Statistics
and Machine Learning Toolbox. The two main functions used were fitcsvm for binary
classification and fitcecoc for multiclass classification. Both tools support predictor data
mapping with the use of kernel functions, and can employ SVM solvers like Sequential
Minimal Optimization (SMO) which is a fast algorithm for training SVMs (Platt, 1998).
Moreover, fitcecoc also provides multiclass learning by producing an error-correcting
output codes (ECOC) classifier that combines binary classifiers in order to solve a multiclass
problem (Escalera, Pujol & Radeva, 2010).
Each SVM model was produced using the template properties shown in Table 1. From
the dataset collected, the function fitcsvm was implemented to train an SVM classifier for
discriminating Latin and Babayin characters. Training an SVM classifier to differentiate
dot and bar from cross and x for the Baybayin diacritics classifier was similarly carried out.
An SVM multiclass classification model was trained for each script (Baybayin and Latin
characters) using the fitcecoc function. The experiments were carried out with 20% and
30% holdout option for testing, i.e., 80% and 70% of the gathered data were used to train
the models.
In the multiclass setting, the model’s performance on each character was measured.
Figure 9 shows the performance results (accuracy, precision, recall, and F1 score) on
each of the ten independent runs for each classifier (script, Latin character, and Baybayin
character) in the testing phase, for 20% and 30% holdout samples.
Meanwhile, the generalization performance for the Baybayin diacritics showed 0%
classification error in both holdout options. This result can be attributed to the symbols’
unique structures compared to other characters.
The mean and standard deviation of the generalization performance results were
also calculated for each holdout options (see Table 2). Empirical results show that the
20% holdout percentage yields better generalization performance than the 30% holdout
percentage.
It is noteworthy that most of the errors came from the characters with similar structures.
For example, small letter ’m’ and capital letters ‘I’ and ‘T’ from Latin were sometimes
recognized as Baybayin characters ‘Na’, ‘Ka’, and ‘La’, respectively (see Fig. 1). Also, in
Pino et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.360 16/24
Figure 9 Generalization Performance Results. The generalization performance (accuracy (A), precision
(B), recall (C), and F1 score (D)) of each classifier (script, Latin character, and Baybayin character) is mea-
sured per iteration, for 20% and 30% holdout samples.
Full-size DOI: 10.7717/peerjcs.360/fig-9
Table 2 Mean and standard deviation (SD) of the generalization performance results for 10 iterations in the testing phase with 20% and 30%
holdout options. Better performance results between the two holdout options are styled in bold.
Classifiers Holdout Accuracy Precision Recall F1 Score
Percentage Mean SD Mean SD Mean SD Mean SD
20% 98.56 0.08 98.56 0.08 98.55 0.08 98.56 0.08
Script 30% 98.44 0.09 98.44 0.09 98.43 0.09 98.43 0.09
20% 95.79 0.32 95.81 0.31 95.79 0.32 95.80 0.32
Baybayin
Characters 30% 95.43 0.21 95.43 0.20 95.43 0.21 95.43 0.21
20% 96.07 0.34 96.11 0.34 96.07 0.34 96.10 0.34
Latin
Characters 30% 95.54 0.24 95.58 0.24 95.54 0.24 95.56 0.24
Latin character identification, most errors came from letter ’I’ being identified as either ‘L’
or ‘J’.
Figure 10 reports the model’s accuracy (diagonal cells), precision (rightmost
column), and recall (bottom row) for each Baybayin character. It can be observed that
Pino et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.360 17/24
Figure 10 Generated confusion matrix from the best Baybayin character classifier. The diagonal en-
tries correspond to the number of correctly classified characters, while the off-diagonal entries corre-
spond to the number of incorrectly classfied characters. The last column and the last row correspond to
the precision and recall of each character, respectively. The bottom right entry is the overall accuracy of
the model.
Full-size DOI: 10.7717/peerjcs.360/fig-10
misclassifications occured among the characters ‘Pa’, ‘Ma’, ‘Ya’, and ‘A’, since they have
very similar forms (see Fig. 1). To solve this problem, a reconsideration classifier was added
to the main body classification process for these ambiguous characters. The best models
were obtained with their respective accuracy: ‘A’ vs. ‘Ma’ –97.75%; ‘Ka’ vs. ‘E’/‘I’ –99%;
’Ha’ vs. ‘Sa’ –99.5%; ‘La’ vs. ‘Ta’ –99%; and ‘Pa’ vs. ‘Ya’ –94.50%. Figure 11 shows an
example of Baybayin and Latin character images that are fed into the proposed system and
produced a correct classification result (see Figs. 1 and 2for verification).
To further test the performance of the proposed system on Baybayin characters, the
images that satisfy the system’s assumptions (see ‘Proposed OCR System’) were selected
from the gathered dataset in Nogra (2019). Among the selected images, 1,100 were randomly
chosen for the test runs and were fed into the proposed OCR system for recognition. The
conducted experiment obtained an overall average of 98.41%, 98.68%, 98.45%, and 98.57%
for accuracy, recall, precision, and F1 score, respectively.
CONCLUSIONS AND FUTURE WORKS
This study has largely contributed to the script and character recognition community by
providing a new set of data for Baybayin characters, its diacritics, and Latin characters
Pino et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.360 18/24
Figure 11 System framework for Baybayin and Latin characters. (A) Character Ko/Ku enters the sys-
tem. (B) Letter R enters the system.
Full-size DOI: 10.7717/peerjcs.360/fig-11
that can be used in future related studies. This paper also introduced an OCR system
that utilizes SVM in discriminating Baybayin script from Latin, and in classifying each of
the writing system’s characters. A limitation of this study is that the Baybayin characters
written in a manner where the diacritics are attached to the main body were not considered.
This setting is sensible since Baybayin characters are generally written with the diacritics
detached from the main body. An advantage of this limitation though is that the number
of classes in the SVM multiclass algorithm was significantly reduced.
Another strong point of this study is that the proposed system accommodates the
highly similar structures among the Baybayin characters, yielding higher generalization
performance. Experimental results also show that SVM can be an effective tool in Baybayin
character recognition.
For future works, one can explore the use of multiclass SVM in classifying Baybayin
characters where the characters with accents are treated as separate classes. One can also
explore other machine learning algorithms to solve the classification problems arising
from the proposed OCR. A comprehensive comparative study of different classification
algorithms applied to Baybayin scripts is also an interesting research direction. Similar
to what was shown in Phangtriastu, Harefa & Tanoto (2017), other feature extraction
algorithms can be combined with SVM to identify which will work well in classifying
Baybayin scripts.
Pino et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.360 19/24
Improving the number of images in the dataset may also significantly decrease the
classification error. The Baybayin characters considered in this study are based on the
traditional scripts only. Modern Baybayin characters are being proposed recently to
conform with the modern Filipino alphabet. Inclusion of these modern Baybayin characters
in the proposed OCR system is another interesting extension of this study.
ADDITIONAL INFORMATION AND DECLARATIONS
Funding
This work was supported by the Office of the Chancellor of the University of the
Philippines, through the Office of Vice Chancellor for Research and Development,
through the Outright Research Grant (Project No. 202025 ORG). R. Pino acknowledges
the Department of Science and Technology - Science Education Institute (DOST-SEI)
through the Accelerated Science and Technology Human Resources Development Program
(ASTHRDP) Scholarship, for funding support. The funders had no role in study design,
data collection and analysis, decision to publish, or preparation of the manuscript.
Grant Disclosures
The following grant information was disclosed by the authors:
The Office of the Chancellor of the University of the Philippines, through the Office of Vice
Chancellor for Research and Development, through the Outright Research Grant: 202025
ORG.
Competing Interests
The authors declare there are no competing interests.
Author Contributions
•Rodney Pino conceived and designed the experiments, performed the experiments,
analyzed the data, performed the computation work, prepared figures and/or tables,
authored or reviewed drafts of the paper, and approved the final draft.
•Renier Mendoza and Rachelle Sambayan conceived and designed the experiments,
analyzed the data, authored or reviewed drafts of the paper, and approved the final draft.
Data Availability
The following information was supplied regarding data availability:
The data used in this article can be found at Kaggle. Specifically:
- Baybayin images: https://www.kaggle.com/rodneypino/baybayin-and-latin-binary-
images-in-mat-format?select=Baybayin
- Latin images: https://www.kaggle.com/rodneypino/baybayin-and-latin-binary-
images-in-mat-format?select=Latin
- Baybayin diacritics images: https://www.kaggle.com/rodneypino/baybayin-and-latin-
binary-images-in-mat-format?select=Baybayin+Diacritics
The MATLAB code is available in GitHub:
https://github.com/rbp0803/An-OCR-System-for-Baybayin-Scripts-using-SVM.
Pino et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.360 20/24
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