Recognition of Words in Tamil Script Using Neural Network

M. Karthigaiselvi. Int. Journal of Engineering Research and Application www.ijera.com
ISSN : 2248-9622, Vol. 7, Issue 3, ( Part -6) March 2017, pp.62-70
www.ijera.com DOI: 10.9790/9622-0703066270 62 | P a g e
Recognition of Words in Tamil Script Using Neural Network
M. Karthigaiselvi1
, T. Kathirvalavakumar2
Research Centre in Computer Science, V. H. N. Senthikumara Nadar College, Virudhunagar-626 001, Tamil
Nadu, India
ABSTRACT
In this paper, word recognition using neural network is proposed. Recognition process is started with the
partitioning of document image into lines, words, and characters and then capturing the local features of
segmented characters. After classifying the characters, the word image is transferred into unique code based on
character code. This code ideally describes any form of word including word with mixed styles and different
sizes. Sequence of character codes of the word form input pattern and word code is a target value of the pattern.
Neural network is used to train the patterns of the words. Trained network is tested with word patterns and is
recognized or unrecognized based on the network error value. Experiments have been conducted with a local
database to evaluate the performance of the word recognizing system and obtained good accuracy. This method
can be applied for any language word recognition system as the training is based on only unique code of the
characters and words belonging to the language.
Keywords: Segmentation, Future extraction, Classification, Word recognition, Neural network, Back-
propagation
I. INTRODUCTION
Artificial neural networks [12] have been
extensively applied for document analysis and
recognition. Most efforts have been devoted for the
recognition of isolated handwritten and printed
characters with widely recognized successful
results.
Ho et al. [6] have proposed a method for
word recognition in degraded images of machine-
printed postal addresses on envelopes based on
word shape analysis. Allen et al. [1] have
demonstrated a Holistic off-line handwritten word
case recognition using a multi layer perceptron
consisting of all lowercase or all uppercase
characters. Zhu and Hull [20] have presented an
algorithm for word recognition in oriental language
documents. This technique compared the feature
vectors extracted from sequences of characters
directly to the feature vectors for words. Lavrenko
et al. [9] have presented a holistic word recognition
approach for single-author historical documents.
The recognition output can then be used to align
lexicon terms and their respective location in the
page image.
Yaeger et al. [18] have combined an
artificial neural network (ANN) character classifier
with context-driven search over character
segmentation, word segmentation, and word
recognition hypotheses to provide robust
recognition of hand-printed English text in new
models of Apple Computer's Newton Message Pad.
Cho et al. [4] have presented a new method for
modeling and recognizing cursive words with
hidden Markov models (HMM). In the method,
sequences of thin fixed-width vertical frames are
extracted from the image, capturing the local
features of the handwriting. By quantizing the
feature vectors of each frame, the input word image
is represented as a Markov chain of discrete
symbols.
Seni and Nasrabadi [16] have presented a
system for writer independent large vocabulary
recognition of on-line handwritten cursive words.
The network recognizer avoids explicit
segmentation of the input words by using a sliding
window concept. Bharath and Madhvanath [2] have
proposed a data-driven HMM-based online
handwritten word recognition system for Tamil.
Steinherz et al. [17] have reviewed the field of
online cursive word recognition. They classify the
field into three categories: segmentation-free
methods, which compare a sequence of
observations derived from a word image with
similar references of words in the lexicon;
segmentation-based methods, that look for the best
match between consecutive sequences of primitive
segments and letters of a possible word; and the
perception-oriented approach, that relates to
methods that perform a human-like reading
technique, in which anchor features found all over
the word are used to boot-strap a few candidates for
a final evaluation phase.
Lecolinet et al. [10] have presented
methods and strategies for cursive word
recognition. Lu et al. [11] have proposed a new
word shape coding scheme, which captures the
document content through annotating each word
RESEARCH ARTICLE OPEN ACCESS

image by a word shape code including character
ascenders/descenders, character holes, and
character water reservoirs. Moghaddam et al. [13]
have presented a system for preprocessing and
word spotting of very old historical document
images and is language independent. Document
images are processed for extraction of salient
information using a word spotting technique which
does not need line and word segmentation. Frinken
et al. [5] have presented a novel word spotting
algorithm using BLSTM (Bidirectional Long
Short-Term Memory) neural networks. Zhang and
Tan [19] have presented a word image coding
technique to extract features from each word image
object and represent them using feature code
strings for comparison. A novel word image
annotation technique is presented which captures
the document content by converting each word
image into a word shape code. In particular, we
convert word images by using a set of topological
character shape features including character
ascenders/ descenders, character holes, and
character water reservoirs. Huang et al. [7] have
proposed a word shape recognition method for
retrieving image-based documents. The method
detects local extrema points in word segments to
form so-called vertical bar patterns. These vertical
bar patterns form the feature vector of a document.
Recognition of words in Tamil script using
neural network is proposed in this paper. The
recognition process is having more complexity
because of segmentation problems. Due to these
difficulties many of the previous approaches have
failed to recognize the words correctly. But in this
work, before recognize the words; problems in
touching line segmentation and touching character
segmentation are solved. So the result of
recognition is promising. The rest of the paper is
organized as follows: Section 2 describes the
characteristics of Tamil script; Section 3 elaborates
the preprocessing techniques that are performed to
enhance the document image; Section 4 describes
word segmentation procedure; Section 5 details the
feature extraction and character classification;
Section 6 describes word recognition; Section 7
describes the neural network training; Section 8
discusses the experimental results and Section 9
describes the conclusion.
II. CHARACTERISTICS OF TAMIL SCRIPT
Tamil is a widely spoken South Indian
language with 247 characters (Fig. 1). It contains
12 vowels, 18 consonants, 1 special character and
216 compound characters. There are also symbols
called modifiers (Table 1) those occupy specific
positions around the base characters. When the
modifiers that get added on the left or right side of
the base character it remains disjoint from the base
character, but when those are added either at the
top or bottom of the base characters those get
connected and spread to the upper and lower zones
respectively.
Fig. 1: Vowels and consonants of Tamil script
a. Text line structure
A text line of Tamil script can be
partitioned horizontally into three zones namely
upper, middle and lower. Assumed four imaginary
lines namely upper line, mean line, base line and
lower line as shown in Fig. 2 are existing. The
mean line is a horizontal line passes through
maximum number of upper most points of the
characters of a line and base line is the horizontal
line passes through maximum number of
lowermost points of the characters of a line. In the
text line, upper line joins the top of ascenders and
lower line joins the bottom of descenders. The
upper zone denotes the portion above the mean
line, the middle zone covers the region below the
mean line and above the base line, and the lower
zone is the portion where modifiers can reside [3].
The upper zone is separated from the middle zone
of a text line by mean line, and the middle zone is
separated from the lower zone by base line.

Fig. 2: Text line structure
III. PREPROCESSING
The input RGB image is converted into
gray scale image. Smoothing is performed on the
grayscale image to reduce the amount of high
frequency noise using Wiener filter. The well-
known adaptive thresholding Niblack’s approach
[14] is used for binarizing the image.
Gray scale pixel values are used in
detecting skew angle. The upper left corner point is
compared with upper right corner point to find out
whether the document is left skewed or right
skewed. A reference line is drawn from upper left
corner point to lower left corner point. The skew of
the document angle (θ°) is found by finding the
orientation of the reference line. The document is
rotated (θ°) in the anti-clockwise direction to
correct the skew. Here point refers row, column of
particular position. The proposed system can
handle documents with skew angle between +45º
and -45º. Slant removal technique proposed by
Parvez and Mahmoud [15] is used to remove the
slant on the segmented line when the characters are
slanted to the right or left depending on the font
style.
IV. WORD SEGMENTATION
To segment the text lines from the
document image, the horizontal projection profile
of the image is computed. Row with zero
projection is used to segment the text lines.
Sometimes lower zone characters of a line touches
the upper zone characters of next line thus
producing horizontally overlapping lines. The
horizontally overlapping lines make the line
segmentation more difficult. It becomes difficult to
estimate the exact position of a row which
segments a line from the next line. To segment this
kind of overlapped lines Projection Based Lines
Segmentation (PBLS) [8] is used. There is a
possibility to have two or more overlapped lines
when the strip has projection value greater than the
one-third of average projection values.
Observations reveals that rows with modifiers
(Listed in Table 1) are with minimum projection
values and hence rows with modifiers are not
considered for segmentation but a row with
minimum horizontal projection is identified from
the remaining rows and then the overlapped lines
are separated into individual lines.
Vertical projection profile is used for word
segmentation. In the first step the distance between
adjacent characters in the text line image are
computed. In the second step the computed
distances are classified as either inter-word
distances or inter-character distances using
threshold value. The distances between words are
always larger than distances between characters.
Words can be segmented by comparing the
distances with a suitable threshold. The threshold is
defined as
When the distance value is greater than the
threshold it is treated as a word gap otherwise it is a
character gap. Words are segmented using the word
gap.
V. FEATURE EXTRACTION AND
CLASSIFICATION
Character segmentation is done after the
individual words are segmented. Vertical white
spaces serve to separate successive characters.
Vertical projection method is used to split the
words into sub images of individual characters.
Sometimes body of one character touches the body
of another character thus producing touching
characters. To identify the touching characters
char_threshold (CT) has been defined as 175%
minimum width of the separated character. If the
width of the separated character is greater than CT
then the character have two or more characters.
Touching characters are treated as a single
character which leads to failure in character
recognition. A column with minimum vertical
projection is used to segment the touching
characters. It has been identified from the
experimental observations that minimum vertical
projection values are appeared on the left most
columns, right most columns and also appeared on
the touching places. It makes difficult to estimate
the exact position to segment the touching
characters. But the touching characters are
separated into individual characters by identifying
a column with minimum vertical projection after
ignoring the first and last column.

After separating the characters each
character image is trimmed; vertical and horizontal
projections are applied on the trimmed characters
to identify feature vectors. Structural run based
feature vector technique is used to extract structural
features from printed Tamil characters. Identifying
or recognizing Tamil characters need to identify
modifier features existing on lower and upper part
of the characters. Based on the structural properties
of upper and lower modifiers, characters are
divided into various categories such as upper,
middle, lower and three zone characters
respectively. The upper and lower zone may
contain some modifiers. It has been identified that
four modifiers are in the upper zone and twelve
modifiers are in the lower zone as shown in Table
1. The features namely number of loops, number of
objects, number of runs at first row, number of runs
at last row, number of vertical lines and number of
horizontal lines, tetra bit features, height of the run
at last column and width of the last row of the
trimmed image are extracted to identify and
distinguish every middle zone portion of the
character. Feed forward neural network is chosen
for feature classification. The extracted features of
upper, middle and lower modifiers are used to train
the network using standard back propagation
algorithm.
Table 1: Categories of Upper & Lower zone modifiers
VI. WORD RECOGNITION
Recognizing each word of printed Tamil
document requires a collection of all possible valid
words in a database. Unique number is assigned as
a word code for each word in a database to
recognize them. Each digit of a number is
represented by its binary coded decimal (BCD)
equivalent. Feedforward neural network system is
designed to recognize individual word. It needs to
receive individual characters of each word for
processing. 124 different characters are identified
in the Tamil script. Unique numbers is assigned to
all possible characters of a Tamil script from 1 to
124 as a character code and are shown in Table 2
and its corresponding 7 bit binary value is used in
the neural network. Feedforward neural network is
trained to recognize the words in a database with a
desired accuracy. In the trained network a word is
said to be recognized when the network accuracy is
lesser than or equal to the training accuracy
otherwise it is termed as unrecognized word. This
recognition system is font free and size free as the
assigned code for character and word is font and
size free.
Table 2: Character code
அ 1 வ் 26 சி 51 மீ 76 று 101
ஆ 2 ழ் 27 ஞி 52 யீ 77 னு 102
இ 3 ள் 28 டி 53 ரீ 78 கூ 103
ஈ 4 ற் 29 ணி 54 லீ 79 ஙூ 104
உ 5 ன் 30 தி 55 வீ 80 சூ 105
ஊ 6 க 31 நி 56 ழீ 81 ஞூ 106
எ 7 ங 32 பி 57 ளீ 82 டூ 107
ஏ 8 ச 33 மி 58 றீ 83 ணூ 108
ஐ 9 ஞ 34 யி 59 னீ 84 தூ 109
ஒ 10 ட 35 ரி 60 கு 85 நூ 110
ஓ 11 ண 36 லி 61 ஙு 86 பூ 111
12 த 37 வி 62 சு 87 மூ 112
க் 13 ந 38 ழி 63 ஞு 88 யூ 113
ங் 14 ப 39 ளி 64 டு 89 ரூ 114
ச் 15 ம 40 றி 65 ணு 90 லூ 115
ஞ் 16 ய 41 னி 66 து 91 வூ 116

ட் 17 ர 42 கீ 67 நு 92 ழூ 117
ண் 18 ல 43 ஙீ 68 பு 93 ளூ 118
த் 19 வ 44 சீ 69 மு 94 றூ 119
ந் 20 ழ 45 ஞீ 70 யு 95 னூ 120
ப் 21 ள 46 71 ரு 96 121
ம் 22 ற 47 ணீ 72 லு 97 122
ய் 23 ன 48 தீ 73 வு 98 123
ர் 24 கி 49 நீ 74 ழு 99 124
ல் 25 ஙி 50 பீ 75 ளு 100
VII. NEURAL NETWORK TRAINING
Recognizing all words in a database is the
task of the neural network. Every word in the
database is used for training. Trained and untrained
words are identified by the trained network from
the network error. Single hidden layer feed forward
neural network is considered for this system.
Number of neurons in the input layer is the product
of length of the longest word in the data base and
character code length. Number of neurons in the
output layer is the product of number of digits in
the size of the word database and length of a BCD
form of a digit.
All input patterns to be represented in
uniform length. For smaller length words, bit ‘0’s
are appended as a suffix in the pattern. For example
if the word to be processed is ‘அரிது’ and
maximum length word in a database is 10 then
input pattern of a corresponding word ‘அரிது’ is
0000001 0111100 1011011 0000000 0000000 0000000 0000000
0000000 0000000 0000000. Every word code is
represented in uniform length. Required numbers
of zeroes are prefixed with the code to keep the
word length uniform. For example if a code of a
word is 2 and size of the word database is 500 then
the code 2 is represented as 002 and its BCD is
0000 0000 0010 which is the word code and is a
target value of the word during network processing.
If the code of a word is 459 then it is represented as
0100 0101 1001.
Standard backpropagation algorithm is
used for network training. Mean square error is
used as a measure for termination during training.
A word is treated as recognized by the trained
network if the network error is lesser than or equal
to the termination condition error during training
otherwise the word is treated as unrecognized.
VIII. EXPERIMENTAL RESULTS
Experiments are carried out with images
of printed pages obtained from different Tamil
literary periodicals collected from [21-26].
Documents are with single column text regions and
with pdf format. These documents are initially
converted into jpeg image format. 1000 different
documents have been taken with different level of
skew, slant as well as size. The proposed method is
implemented in Matlab13.
Segmentation process is applied on the
preprocessed document. In line segmentation,
touching lines are identified and are segmented
correctly. For example, the document specified in
Fig. 3(a) has 5 lines but only 4 line breaks are
recognized by projection profile technique as
shown in Fig. 3(b). It concludes that the document
is with overlapping lines. The method PBLS
processed the document shown in Fig. 3(a) and
segment it into 5 strips correctly as in Fig. 3(c).
Fig 3: (a) Original Image of document15 (b) Strips obtained using horizontal projection (c) Identified Lines
using PBLS method

Fig. 4: (a) Original image (b) Segmented words
The space between the words is greater than the space between the characters. The line specified in
Fig.4 (a) is segmented into 5 words successfully as shown in Fig.4 (b).
Fig. 5: Splitting of touching characters
Vertical projection segments the word
into 5 partitions (17) (10)
(45), (13) and (18) which is shown in Fig. 5
(value within the parentheses specifies the width of
the character). CT of the word is computed as 21.
The third partition ( ) has greater width value
than CT. It implies that it is with touched characters
and requires segmentation. As a result of splitting
procedure the touched characters are split into
individual characters and
After character segmentation, features are
extracted from all characters in a word, which is
then used in the neural network for classification.
Features extracted from upper, middle, lower and
three zone characters are illustrated in Table 3 with
sample characters.
In order to test the performance of the
proposed word recognition procedure, set of 500
word images with different fonts and sizes are
collected from documents. The maximum word
length of the dataset is 10. After character
classification, each word image is encoded by
concatenating binary value of consecutive character
code of a word. The encoded words are input
patterns of the network. Assign values 1 to 500 as
word code for those words. Each word code is
converted into BCD code of uniform length. The
word “ ” during training is transformed
into input pattern as follows. The first character ‘ ’
in the word is coded as 44, the second character
‘ ’ is represented as 18, the third ( ) fourth ( ),
fifth ( ) and sixth ( ) characters are coded as 36,
14, 31 and 28 respectively. The maximum length of
the word in the dataset is 10, but the word has only
6 characters. To keep uniform word length
remaining 4 character codes are set as zeroes. Each
character code is converted into 7 bit binary values.
Totally we get 70 bits to represent the word pattern
and is shown in Fig.6. The target value of the word
is 65. Size of the database is 500 (three digit), so
every word code needs 3 digit length. The word
code 65 is represented as 065 and then it is
converted into its BCD equivalent 0000 0110 0101.
Fig. 6: Code representation for sample word
Single hidden layer feedforward neural
network is trained with standard backpropagation
algorithm for word recognition. Number of neurons
in the input layer is 70. Number of neurons in
Hidden Layer is set as 151 by trial and error.
Number of output neuron is 12. The experiment is
executed with the learning parameters λ =0.05.
This value is fixed by trial and error. Termination
condition is fixed as 0.01 mean squared error
(MSE). Among 500 words in the database 300
words are randomly chosen for training. Remaining
200 words and 100 words used in training are used
for testing. Learning curve of the network training
is shown in Fig 7. Testing of the trained network
shows that the MSE of the non-trained patterns are
greater than 0.01 and MSE of the trained patterns
are lesser than 0.01 and are shown in Fig 8.
Termination condition (0.01 MSE) used in the
training phase is marked as error accuracy in Fig 8.
The experiment is repeated with different number
of randomly selected training and testing patterns
with required learning parameter and the results
obtained are shown in Table 4. In each experiment
all the patterns used in testing phase are recognized
as trained or untrained pattern based on the
network error.

0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
1
0
1
1
1
0
0
1
1
0
0
1
1
1
1
0
1
0
1
0
1
1
1
1
1
1
1
0
1
0
1
0
1
1
1
2
2
0
3
1
1
1
1
2
1
2
1
1
1
1
3
1
1
1
2
0
0
2
0
0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
1
2
3
4
Table3:Differentfeaturesextractedfromupper,middle,lowerandthreezonecharacters

Fig.7: Learning Curve for word recognition
Fig. 8: network error of trained and untrained patterns during testing phase
Table 4. Word recognition results
S.
No
#
Training
patterns
Network
structure
Learning
paramete
r
Terminatio
n condition
(MSE)
#
Epoch
s
Time (s) Testing patterns Test
Accurac
y
(%)
# Trained
patterns
# Test
pattern
s
1 200 70-121-12 0.05 0.01 844 1662.412140 100 300 100%
2 300 70-151-12 0.05 0.01 1060 4009.372474 100 200 100%
3 400 70-171-12 0.07 0.01 1428 4612.960061 100 100 100%
IX. CONCLUSION
In this paper a single hidden layer neural
network is used for recognizing printed Tamil
script words. All Tamil characters are given unique
code and similarly all words in the database are
given unique code and are used in training. After
characters of the words are recognized, words are
recognized as trained word or untrained word using
network. The proposed word recognition process is
simple and gives correct recognition. It is style and
size (word length) independent. Experiment results
show that any word is recognized by the trained

neural network as trained or untrained based on the
network training accuracy. This leads to the result
that if all valid semantically correct words in the
dictionary of any language are used in the training
phase of the network then the trained neural
network identifies the semantically wrong words
and correct words.
ACKNOWLEDGEMENT
The authors thank the university grants
commission, Government of India for partially
supporting this project (MRP: F.No. 42-
144/2013(SR)).
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