Fast Arabic Glyph Recognizer based on Haar Cascade Classifiers
Ashraf AbdelRaouf
1
, Colin A. Higgins
2
, Tony Pridmore
2
and Mahmoud I. Khalil
3
1
Cloudypedia, Cloud brokerage company, Dubai, UAE
2
School of Computer Science, The University of Nottingham, Nottingham, U.K.
3
Faculty of Engineering, Ain Shams University, Cairo, Egypt
Keywords: Arabic Character Recognition, Document Analysis & Understanding, Haar-like Features, Cascade
Classifiers.
Abstract: Optical Character Recognition (OCR) is an important technology. The Arabic language lacks both the
variety of OCR systems and the depth of research relative to Roman scripts. A machine learning, Haar-
Cascade classifier (HCC) approach was introduced by Viola and Jones (Viola and Jones 2001) to achieve
rapid object detection based on a boosted cascade Haar-like features. Here, that approach is modified for the
first time to suit Arabic glyph recognition. The HCC approach eliminates problematic steps in the pre-
processing and recognition phases and, most importantly, the character segmentation stage. A recognizer
was produced for each of the 61 Arabic glyphs that exist after the removal of diacritical marks. These
recognizers were trained and tested on some 2,000 images each. The system was tested with real text images
and produces a recognition rate for Arabic glyphs of 87%. The proposed method is fast, with an average
document recognition time of 14.7 seconds compared with 15.8 seconds for commercial software.
1 INTRODUCTION
The HCC approach was initially presented in 2001.
(Viola and Jones, 2001) introduced a rapid object
detection algorithm using a boosted cascade of
simple features applied to face detection. Integral
images were introduced as a new image
representation allowing very quick computation of
features. The Haar-like features were extended by
adding rotated features, and an empirical analysis of
different boosting algorithms with improved
detection performance and computational
complexity was presented in (Lienhart, Kuranov et
al., 2002).
Experimental work on the novel application of
the HCC approach to the recognition of Arabic
glyphs is presented. First, we justify the application
of the Viola and Jones approach to Arabic character
recognition, then section 2 briefly reviews the
theoretical basis of the HCC method. An experiment
is then described in which all the Arabic naked
glyphs (Arabic glyphs after the removal of
diacritical marks) are recognised, with the results
presented in section 3. The paper concludes with a
discussion of the usefulness of the HCC approach in
Arabic character recognition in section 4.
1.1 The Challenge of Arabic Character
Recognition
Research in OCR faces common difficulties
regardless of approach. A method is needed to
distinguish ink from non-ink, skew detection and
correction algorithms are needed to correct
rotational scanning error and a normalization
algorithm is also required to scale the document so
input and model glyphs are the same size (Al-
Marakeby, Kimura et al., 2013, Alginahi, 2013).
The Arabic language causes additional
difficulties. It is cursive, so a sophisticated character
segmentation algorithm is needed if the word is to be
segmented into its consituent glyphs. Character
segmentation is one of the bottlenecks of current
Arabic character recognition systems (Abdelazim,
2006, Naz, Hayat et al., 2013). We suggest that
skipping the character segmentation process could
improve Arabic character recognition success rates.
This section provides an overview of Arabic
script and discusses the problems facing the
developer of an OCR application (AbdelRaouf,
350
AbdelRaouf A., A. Higgins C., Pridmore T. and I. Khalil M..
Fast Arabic Glyph Recognizer based on Haar Cascade Classifiers.
DOI: 10.5220/0004925803500357
In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods (ICPRAM-2014), pages 350-357
ISBN: 978-989-758-018-5
Copyright
c
2014 SCITEPRESS (Science and Technology Publications, Lda.)
Higgins et al., 2008, AbdelRaouf, Higgins et al.,
2010).
1.1.1 Key Features of Written Arabic
Arabic script is rich and complex. Most notably:
It consists of 28 letters (Consortium, 2003)
written from right to left. It is cursive even
when printed and letters are connected by the
baseline of the word. No distinction is made
between capital and lower-case letters.
Dots are used to differentiate between letters.
There are 19 “joining groups” (Consortium,
2013), each of which contains multiple similar
letters which differ in the number and place of
dots. For example ( ) have the same
joining group () but different dots. The root
character is referred to as a glyph.
Arabic script incorporates ligatures such as
Lam Alef () which actually consists of two
letters ( ) but when connected produce
another glyph (Alginahi, 2013).
Arabic words consist of one or more sub-
words, called PAWs (Pieces of Arabic Word)
(Slimane, Kanoun et al., 2013, Lorigo and
Govindaraju May, 2006), PAWS without dots
are called naked PAWS (AbdelRaouf, Higgins
et al. 2010)).
Arabic letters have four different shapes
according to their location in the word (Lorigo
and Govindaraju May, 2006); start, middle,
end and isolated.
Arabic font files exist which are similar to the
old form of Arabic writing. For example a
statement with an Arabic Transparent font like
( ) when written in Andalus
old shape font becomes ( اﺔﻘﻳﺪﺤﻟا ﻲﻓ ﺐﻌﻠﻳ ﺪﻤﺣ ).
1.2 Implementing the Haar-Cascade
Classifier (HCC) Approach
The HCC approach (Viola and Jones, 2001) is
implemented in the Open Computer Vision library
(OpenCV). OpenCV is an open source library of
Computer Vision functions. It is aimed at real time
computer vision applications using C/C++ and
Python. (Bradski, 2000; Bradski and Kaehler, 2008).
1.2.1 Faces and Glyphs
The HCC approach was originally intended for face
detection. There are, however, important similarities
between faces and Arabic glyphs:
Like faces, most Arabic glyphs have clear and
distinguishing visual features.
Arabic characters are connected, and
recognition requires individual glyphs to be
picked out from a document image.
Characters can have many font sizes and may
also be rotated, similar to size and orientation
differences in face images.
Facial images may vary considerably,
reflecting gender, age and race. The use of
different fonts introduces similar variations
into images of Arabic glyphs.
Each glyph can be considered a different object to
be detected and having a distinct classifier. That
glyph classifier will detect its glyph, ignoring
others, and so becomes a glyph recogniser.
1.2.2 Training and Testing
Two sets of training images are needed; a positive
set contains images which include at least one target
and a negative set containing images which without
any target objects. A further positive set is required
for testing. Each positive set includes a list file
containing the image name(s) and the position(s) of
the object(s) inside each image.
As each Arabic letter can appear in four locations
(hence four glyphs) a total of 100 datasets and
classifiers are needed.
1.2.3 Advantages
Combining the feature extraction with the
classification stages in HCC facilitates the process of
training and testing the many glyphs that must be
recognised. The HCC approach is scale invariant
and so removes the need for explicit normalization.
Using extended, rotated features also removes the
need for skew detection and correction. HCC is
applied directly to grey-scale images, removing the
need for a binarization algorithm. This leads to a
segmentationless process.
2 THEORETICAL BACKGROUND
The HCC is a machine learning approach that
successfully combines three basic ideas. The first is
an image representation that allows the features to
be computed very quickly (integral image).
Secondly, an extensive set of features that can be
computed in a short and constant time. Finally, a
cascade of gradually more complex classifiers
results in fast and efficient detection (Viola and
Jones 2001, Kasinski and Schmidt 2010, Wang,
Deng et al., 2013).
FastArabicGlyphRecognizerbasedonHaarCascadeClassifiers
351
2.1 Integral Image
Integral images were first used in feature extraction
by (Viola and Jones, 2001). (Lienhart and Maydt,
2002) developed the algorithm by adding the rotated
integral image (Summed Area Table - SAT). This is
an algorithm for calculating a single table in which
pixel intensity is replaced by a value representing
the sum of the intensities of all pixels contained in
the rectangle. This is defined by the pixel of interest
and the upper left corner of the image (Crow, 1984).
The integral image at any location x,y is equal
to the sum of the pixels’ intensities (grey-scale) from
the 0,0 to x,y as shown in Figure 1 (a) and is
known as SATx,y.
SAT
x,
y
Ix
,
y
,
(1)
The RecSumr for the upright rectangle
r
x,y,w,h,0°
as shown in Figure 1 (b) is:
RecSum
r
SAT
x1,
y
1
SAT
xw1,
y
h
1
SAT
xw1,
y
1
SAT
x1,
y
h1
(2)
The integral image for the 45° rotated rectangle
at any location x,y is equal to the sum of the
pixels’ intensities at a 45° rotated rectangle with the
bottom most corner at x,y and extending upward
till the boundary of the image as shown in Figure 1
(c) and is known as RSATx,y.
RSATx,
y
∑
^,^|^|
Ix^′,
y
^′
(3)
Figure 1: Image illustration of SAT and RSAT.
The RecSum (r) for the upright rectangle
,,,,45°
as shown in Figure 1 (d) is:
,
1
,1
,1
,1
(4)
2.2 Haar-like Feature Extraction
Haar-like feature extraction captures basic visual
features of objects. It uses grey-scale differences
between rectangles in order to extract object features
(Viola and Jones, 2001). Haar-like features are
calculated by subtracting the sum of a sub-window
of the feature from the sum of the remaining window
of the feature (Messom and Barczak, 2006). The
Haar-like features are computed in short and
constant time
Following (Lienhart, Kuranov et al., 2002) we
assume that the Haar-like features for an object lie
within a window of of pixels, which can be
defined in Equation 5:
features ω
∙RecSum
r
(5)
where ω
is a weighting factor which has a
default value of 0.995 (Lienhart, Kuranov et al.,
2002). A rectangle is specified by five parameters
r
x,y,w,h,α
and its pixel sum is denoted by
RecSum
r
as explained in Equations 2 and 4. Two
examples of such rectangles are given in Figure 2.
Figure 2: Upright and 45° detection windows.
Equation 5 generates an almost infinite feature
set, which must be reduced in any practical
application. The 15 feature prototypes are shown in
Figure 3: (1) four edge features, (2) eight line
features, (3) two centre-surround features, and (4) a
special diagonal line feature. This set of features was
scaled in the horizontal and vertical directions. Edge
features (a) and (b), line features (a) and (c) and the
special diagonal line feature were first used in
(Papageorgiou, Oren et al., 1998, Mohan,
Papageorgiou et al., 2001, Viola and Jones, 2001).
They took as the value of a two-rectangle feature
(edge features) the difference between the sum of
the pixels in the two regions. A three-rectangle
feature (line features) subtracts the sum of the two
outside rectangles from the sum of the middle
rectangle. A four-rectangle feature (special diagonal
ICPRAM2014-InternationalConferenceonPatternRecognitionApplicationsandMethods
352
line feature) subtracts the sum of the two diagonal
pairs of rectangles (as in Figure 3).
(Lienhart and Maydt, 2002) added rotated
features, significantly enhancing the learning system
and improving the classifier performance. These
rotated features were significant when applied to
objects with diagonal shapes, and are particularly
well suited to Arabic character recognition.
The number of features differs among
prototypes. E.g., a 2424 window gives 117,941
features (Lienhart, Kuranov et al. 2002).
(1) Edge features (2) Line features
(3) Centre-surround
features
(4) Special diagonal line
feature
Figure 3: Haar-like feature prototypes.
2.3 Cascade of Boosting Classifiers
A classifier cascade is a decision tree that depends
upon the rejection of non-object regions (Figure 4).
Boosting algorithms use a large set of weak
classifiers in order to generate a powerful classifier.
Weak classifiers discriminate required objects from
non-objects simply and quickly. Only one weak
classifier is used at each stage and each depends on a
binary threshold decision or small Classification
And Regression Tree (CART) for up to four features
at a time (Schapire 2002).
3 EXPERIMENTS
A successful pilot experiment made to investigate
the HCC approach for Arabic glyphs and test the
methods applicability. A single Arabic letter, Ain
(), was used in its isolated form. 88.6% recognition
was achieved which showed that the HCC approach
is suitable for printed Arabic character recognition.
Figure 4: Cascade of classifiers with N stages.
3.1 Planning the Experiment
The hypothesis to be tested is that HCC allows the
pre-processing, binarization, skew correction,
normalisation and segmentation phases typically
associated with OCR to be skipped. The
experimental steps are:
The binarization and noise removal step is
skipped and the original grey-scale images
used.
The approach deals with the basic and rotated
features of the glyphs so there is no need for
the skew detection and correction step. (For
this reason an application was designed to
generate rotated images for testing).
The text lines detection step is skipped. Each
glyph is detected along with its location in the
document image implying the location of the
lines.
The normalization step is not needed because
the HCC approach is scale invariant. For that
reason, different font sizes are used and tested.
The character segmentation phase can be
omitted when using the HCC approach. Thus
the system was trained and tested using real
Arabic document images.
The following aspects were addressed in the
experiment:
Naked glyphs were used in the experiment to
reduce the number of classifiers generated for
classification which also reduces the
recognition duration. It is easy to later locate
and count the dots in the recognized glyph
(Abdelazim, 2006).
There are 18 naked Arabic letters (Unicode
1991-2006). (AbdelRaouf, Higgins et al.
2010) showed that adding Hamza () and Lam
Alef () is essential; Table 1 shows all the
naked Arabic glyphs used in the experiment.
3.2 Data Preparation
The datasets used in the experiment are images of
real and computer generate Arabic documents which
act as both negative and positive images. Negative
(c) (d)(a) (b)
(a) (b) (c) (d)
(e) (f) (g) (h)
(a)
(b)
FastArabicGlyphRecognizerbasedonHaarCascadeClassifiers
353
Table 1: Arabic naked glyphs as used in the experiment.
Letter English
name
Arabic
Letter
Isolated
glyphs
Start
glyphs
Middle
glyphs
End
glyphs
ALEF
BEH
HAH
DAL
REH
SEEN
SAD
TAH
AIN
FEH
QAF
KAF
LAM
MEEM
NOON
HEH
WAW
YEH
HAMZA
LAM ALEF
and positive images were generated for each glyph.
The MMAC corpus (AbdelRaouf, Higgins et al.,
2010) provided data for this experiment. The data
originated from 15 different Arabic document
images. Five of these documents were from scanned
documents (Real data), five documents were
computer generated (Computer data), and the
remaining five were computer generated with
artificial noise added (Noise data). The Computer
and Noise data used different Arabic fonts, sizes,
bold and italic.
3.2.1 Creating Positive and Negative Images
The dataset required for each of the 61 glyphs in this
part of the experiment are:
Positive images: This set of images is
separated into two sub-sets; one for training
and the other for testing. The testing sub-set
accounted for 25% of the total positive
images, while the training sub-set was 75%
(Adolf 2003). Figure 5 (a) shows a sample of
a positive image for the Heh middle glyph
().
Negative images: These are used for the
training process of the classifiers. Figure 5 (b)
shows a sample of a negative image for Heh
middle ().
A program separated the positive from the
negative images for each glyph. The Objectmaker
utility offered in OpenCV (OpenCV, 2002) was used
to manually define the position of the glyph in each
positive document image. This process was very
labour intensive and time consuming, but produced
an excellent research resource.
(a)
A positive image of Heh
middle ()
(b)
A negative image of Heh
middle ()
Figure 5: Sample of positive and negative image for Heh
middle glyph (
).
For the 61 glyphs, the total number of positive
images was 6,657 while the total number of negative
images was 10,941. The relationship between the
total numbers of positive and negative images for
each glyph shows the following three different
categories of Arabic glyphs:
Glyphs that exist in almost all of the images.
These glyphs are Alef isolated (), Alef end ()
and Lam start (). These have very few
negative images, so more were generated by
manually masking out images that contained
only one or two glyphs. Figure 6 (a) shows a
positive image of the glyph Alef end. Figure 6
(b) shows the same document after converting
it to a negative image.
(a)
A positive document
image of Alef end
(b)
Converting positive
image to negative
Figure 6: Sample of converting a positive image to
negative.
Glyphs that rarely appear in the images and
so have a very small number of positive
images. Any editing here would be too
artificial, however, good results are not
expected from their classifiers (e.g. Tah
isolated ()).
Most of the glyphs have a reasonable ratio of
negative to positive images.
3.2.2 Creating Numerous Samples of
Positive and Negative Images
The experiment requires a huge amount of images
which are not immediately available. Software was
developed to generate more positive and negative
document images from the available test images, see
(Lienhart, Kuranov et al., 2002). This uses two
algorithms; the nearest neighbour interpolation
algorithm (Sonka, Hlavac et al., 1998) to rotate the
images; the Box-Muller transform algorithm (Box
and Muller, 1958) to generate a normal distribution
of random numbers from the computer generated
ICPRAM2014-InternationalConferenceonPatternRecognitionApplicationsandMethods
354
uniform distribution. The document rotation angles
use =5 and = 0. as the mean value and it is
considered to be angle 0 as the scanning process
may be skewed in any direction with the same value.
Sigma () is the standard deviation of the angles (i.e.
=5 means that 68.2% falls within ±5°).
The program produced a total of over 2000
positive and negative images as recommended by
(OpenCV, 2002; Adolf, 2003; Seo, 2008) which
allowed the HCC approach to run properly.
3.3 Training the Classifiers
Preparing the files and folders for the training
process was a lengthy process. The training itself
additionally took around a year. Each training
process took two days on average. As each glyph
has on average 3 different trials and there are 61
glyphs this gives around a year of continuous work
on a single dedicated computer.
3.3.1 Defining Training Parameters
It was important to define the training parameters
before the experiment. The parameters used were the
width, height, number of splits, minimum hit rate
and boosting type. These parameters were chosen
following the results and conclusions of auxiliary
experiments.
Training size of the glyph: This is a very
important issue to address. The optimum width and
height of the training size was empirically
determined to achieve the best classification results.
Experiment showed the optimal value for the sum of
width and height is between 35 to 50 pixels.
The number of splits: This defines which weak
classifier will be used in the stage classifier. If the
number of splits used is one (stump), this does not
allow learning dependencies between features. A
Classification And Regression Tree (CART) is
obtained if the number of splits is more than 1, with
the number of nodes smaller than the number of
splits by one (Barros, Basgalupp et al., 2011). The
default value of number of splits is one.
Minimum hit rate: In order for the training
process to move to the next stage, a minimum hit
rate (TP) needs to be obtained (Viola and Jones,
2001). As the minimum hit rate increases it slows
down the training process. The default value is
0.995. (Lienhart, Kuranov et al., 2002) reported a
value of 0.999 which proved remarkably slow so a
range of 0.995 to 0.997 was used.
Boosting type: The experiment showed that the
GAB boosting type is the best algorithm for Arabic
glyph recognition and is similar to face detection
(Lienhart, Kuranov et al., 2002).
3.3.2 Training Statistics
The total number of positive images for all glyphs is
106,324, the number of negative images is 168,241.
The total number of all glyphs in all positive images
is 181,874. The average number of glyphs in each
positive image is 1.71. The average width of all
glyphs is 18.9 while the average height is 24.9.
There are 1743 positive and 2758 negative images.
Table 2 shows, for each location, the trained
width and height, the total number of positive and
negative images and total number of glyphs.
Table 2: Training information of glyphs in all locations.
Position Isolated Start Middle End
Average Width
20.05
17.90 17.27 19.21
Average Height
25.20
24.64 25.27 24.68
Average No. of Positive images 1,645.5 1,799.8 1,853.1 1,749.1
Average No. of Negative images 3,190.2 2,261.0 2,349.5 2,827.4
Average No. of glyphs 2,603.5 3,450.3 3,500.4 2,807.6
Total No. of Positive images 32,910 19,798 20,384 33,232
Total No. of Negative images 63,805 24,871 25,845 53,720
Total No. of glyphs 52,071 37,953 38,505 53,345
3.4 Testing the Classifiers
The testing process of the experiment was separated
into two distinct parts. The first used the
performance utility available in OpenCV. The
second tested the HCC glyphs classifier against real
commercial software.
The main concerns in the testing process were
the values of True Positive (TP), False Negative
(FN) and False Positive (FP) ratios in all tests
(Kohavi and Provost, 1998). TP is the number or
ratio of the glyphs that were detected correctly. FP is
when glyphs are detected wrongly. FN is when
glyphs are not detected at all even though they exist.
3.4.1 Testing using the OpenCV
Performance Utility
This experiment tried to investigate the influence of
the testing parameters on detection accuracy. Two
parameters have a very big effect on detection
accuracy; the scale factor and the minimum
neighbours (Lienhart, Kuranov et al., 2002; Seo,
2008; Kasinski and Schmidt, 2010).
The detection phase starts with a sliding window
using the original width and height and enlarges the
window size depending on the scale factor (Lienhart,
Kuranov et al., 2002). A suitable scale factor for
Arabic glyph detection was found to be 1.01 or 1.02.
The minimum neighbour is the number of neighbour
FastArabicGlyphRecognizerbasedonHaarCascadeClassifiers
355
regions that are merged together during detection in
order to form one object (Lienhart, Kuranov et al.,
2002). A suitable minimum neighbour value was
found to lie between 1 and 3 inclusive.
Table 3 shows the minimum, average and
maximum number of positive testing images and the
total number of glyphs in the positive images for
each location.
Table 3: The testing information of glyphs in all locations.
Position Isolated Start Middle End
Minimum No. of
Positive images
142 525 527 284
Average No. of Positive
images
533.9 593.6 610.4 559.1
Maximum No. of
Positive images
858 704 752 768
Minimum No. of
glyphs in images
142 560 589 284
Average No. of glyphs
in images
857.6 1,180.4 1,136.5 887.4
Maximum No. of
glyphs in images
3,168 2,376 4,192 2,211
3.4.2 Testing using a Commercial Arabic
OCR Application
Here the HCC approach is compared to well known
commercial Arabic OCR software. This gives
realistic measures of the performance of the HCC
approach. All the glyphs classifiers of the HCC
approach are tested against Readiris Pro 10 (IRIS
2004) which was used in (AbdelRaouf, Higgins et
al., 2010).
A new small sample of Arabic paragraph
documents that represents a variety of document
types was used. The sample includes 37 Arabic
documents with 568 words and 2,798 letters.
The Levenshtein distance algorithm used by
(AbdelRaouf, Higgins et al., 2010) was used to
calculate the commercial software accuracy. The
accuracy of the HCC approach was calculated after
running the performance tool offered by OpenCV to
detect the glyphs using the 61 generated classifiers.
3.5 Experiment Results
Results showed that the HCC approach is highly
successful. High accuracy was produced using the
OpenCV testing utility as well as when compared
with commercial software. Two sets of results were
obtained, one for the OpenCV performance utility
and the other for the commercial software.
3.5.1 Results using the OpenCV
Performance Utility
The four location types give different accuracy for
Table 4: Testing information of glyphs in all locations.
Position Isolated Start Middle End
Minimum TP
73%
81% 74% 74%
Average TP
88%
91% 89% 92%
Maximum TP 100% 97% 97% 100%
Minimum FN 0% 3% 3% 0%
Average FN 12% 9% 11% 8%
Maximum FN 27% 19% 26% 26%
Minimum FP 0% 0% 0% 0%
Average FP 8% 7% 9% 4%
Maximum FP 29% 17% 24% 16%
each glyph. Table 4 shows the statistical results of
TP, FN and FP values in the four different locations.
3.5.2 Results of Testing with the
Commercial OCR Application
The HCC approach achieved marginally better
accuracy (87%) than that of the commercial software
(85%).
3.5.3 Experiment Results Comments
The HCC accuracy achieved (87%) is high at
this stage of the work and proves the validity of the
approach.
The glyphs with a small number of samples
return unreasonable results, as expected (e.g.
most isolated location glyphs).
Glyphs with a balanced number of samples
return good results, for example Beh end ().
The glyphs with complicated visual features
get higher recognition accuracy, as expected
e.g. Hah (), Sad () and Lam Alef ().
Using naked glyphs improved the recognition
rate and reduced the number of classifiers.
The higher the number of glyph samples the
better the recognition accuracy.
4 CONCLUSIONS AND
FURTHER WORK
The HCC approach is applicable to Arabic printed
character recognition. This approach eliminates pre-
processing and character segmentation. A complete,
fast, Arabic OCR application is possible with this
technique (average documentation time for HCC
(14.7 seconds) is comparable with commercial
software (15.8 seconds).
Enhancements can be obtained by keeping the
glyph classifiers continually updated. The training of
the glyphs that have a small number of positive or
negative images can be improved by adding new
document images. Hindu and Arabic numerals for
ICPRAM2014-InternationalConferenceonPatternRecognitionApplicationsandMethods
356
example ( 1 2 3 ) and (1 2 3) and for the Arabic
special characters such as ( ‘ ’ ) could be added.
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