machine recognition of devanagari hand -printed script...
TRANSCRIPT
Research Cell : An International Journal
ISSN: 2229-6913 (Print), ISSN: 2320-0332 (Online), Web Presence:
© 2016 Vidya Publications. Authors are responsible for any plagiarism issues.
Machine Recognition
Script: Character and Word Level Analysis
Department of Computer Science and Applications,
Panjab University
Abstract—Various South Asian languages such as Sanskrit, Hindi, Marathi, Nepali etc. are written using Devanagari script and
millions of people in world are using this script for written communication. So there is a high demand of good Devanagari ICR
still not available. An individual having good knowledge of the script of a language can easily read some words writte
pertaining to that script, though these are expressed in very bad manner, on the basis of his/her mental dictionary
be easy to read through a machine. A machine
upper, the lower and the middle. It is difficult to express the size of various regions in hand
bigger or smaller and depends upon a writer’s writing style.
the basis of structural features but in case
deals with the analysis of Devanagari characters and words according
structural features of Devanagari are also discussed.contoured image. The various problems likely to be faced
also discussed.
Keywords- Devanagari; hand-printed; structural, skeleton, contour,
1. Introduction
An ICR works in various stages such as scanning, pre
processing. Each stage has large number of optional techniques which have been used in different recognition
systems. Selecting a best technique for a particular application is a daunting task. One has to exhaustively study the
literature, implement them and observe t
method(s) used must be robust for expressing the properties of a character/script under consideration. If there is slight
variation in character image either due to printing, writing or
same. There are many feature extraction methods available in literature. The features may be local or global. The
features may be extracted from an original character or from its skeleton or contour
stage is concerned, Govindan et al[1] classified the various features in three categories
global transforms and series expansion. Each category has its pros and cons in terms of computatio
computational complicacy and accuracy. In structural based approach, a character is recognized on the basis of
structural primitives from which it is build up and these primitives are also known as character strokes. The number of
such stroke components, the kinds of stroke components and the relationship between these components in some order
is worked out. Some prominent authors have made prompt efforts to use structural features
extraction process from structural feature
Statistical Features are based on the statistical distribution of black and white pixels in a character image. These
features are invariants to character distortion and writing styl
Satish Kumar
Research Cell : An International Journal of Engineering Sciences, Issue June 2016, Vol. 18
0332 (Online), Web Presence: http://www.ijoes.vidyapublications.com
Authors are responsible for any plagiarism issues.
Machine Recognition of Devanagari Hand
: Character and Word Level Analysis
Satish Kumar
Department of Computer Science and Applications,
Panjab University SSG Regional Centre,
Hoshiarpur, Punjab, India
Email: [email protected]
Various South Asian languages such as Sanskrit, Hindi, Marathi, Nepali etc. are written using Devanagari script and
millions of people in world are using this script for written communication. So there is a high demand of good Devanagari ICR
An individual having good knowledge of the script of a language can easily read some words writte
script, though these are expressed in very bad manner, on the basis of his/her mental dictionary
machine-printed word in Devanagari script can be divided into three vertical regions
It is difficult to express the size of various regions in hand-printed i.e. the size
bigger or smaller and depends upon a writer’s writing style. In case of machine-printed it is easy to recognize a script on
the basis of structural features but in case of hand-printed it is difficult due to structural variation in writ
characters and words according to recognition point of view. The various issues related to the
structural features of Devanagari are also discussed. Moreover, the structural features are extracted from skeltonized or
problems likely to be faced while extracting structural features from skeltonized
structural, skeleton, contour, character recognition.
An ICR works in various stages such as scanning, pre-processing, feature extraction, classification and post
Each stage has large number of optional techniques which have been used in different recognition
systems. Selecting a best technique for a particular application is a daunting task. One has to exhaustively study the
literature, implement them and observe their performance. Obviously this is a big task.
method(s) used must be robust for expressing the properties of a character/script under consideration. If there is slight
variation in character image either due to printing, writing or due to instrument used, it should be able to absorb the
same. There are many feature extraction methods available in literature. The features may be local or global. The
features may be extracted from an original character or from its skeleton or contour form. As far as feature extraction
stage is concerned, Govindan et al[1] classified the various features in three categories i.e.
global transforms and series expansion. Each category has its pros and cons in terms of computatio
computational complicacy and accuracy. In structural based approach, a character is recognized on the basis of
structural primitives from which it is build up and these primitives are also known as character strokes. The number of
mponents, the kinds of stroke components and the relationship between these components in some order
Some prominent authors have made prompt efforts to use structural features
is fast [1,2]. Section C covers drawbacks of using structural features.
Statistical Features are based on the statistical distribution of black and white pixels in a character image. These
features are invariants to character distortion and writing styles to some extent. In addition to this the feature vector
178
of Devanagari Hand-printed
: Character and Word Level Analysis
Various South Asian languages such as Sanskrit, Hindi, Marathi, Nepali etc. are written using Devanagari script and
millions of people in world are using this script for written communication. So there is a high demand of good Devanagari ICR but it is
An individual having good knowledge of the script of a language can easily read some words written on a paper
script, though these are expressed in very bad manner, on the basis of his/her mental dictionary. Such words may not
in Devanagari script can be divided into three vertical regions i.e. the
the size of a region can be
printed it is easy to recognize a script on
printed it is difficult due to structural variation in writing. This paper
The various issues related to the
Moreover, the structural features are extracted from skeltonized or
skeltonized or contoured image are
processing, feature extraction, classification and post-
Each stage has large number of optional techniques which have been used in different recognition
systems. Selecting a best technique for a particular application is a daunting task. One has to exhaustively study the
heir performance. Obviously this is a big task. The feature extraction
method(s) used must be robust for expressing the properties of a character/script under consideration. If there is slight
due to instrument used, it should be able to absorb the
same. There are many feature extraction methods available in literature. The features may be local or global. The
As far as feature extraction
statistical, structural and
global transforms and series expansion. Each category has its pros and cons in terms of computational speed,
computational complicacy and accuracy. In structural based approach, a character is recognized on the basis of
structural primitives from which it is build up and these primitives are also known as character strokes. The number of
mponents, the kinds of stroke components and the relationship between these components in some order
Some prominent authors have made prompt efforts to use structural features[6,7,8,13]. The feature
covers drawbacks of using structural features.
Statistical Features are based on the statistical distribution of black and white pixels in a character image. These
es to some extent. In addition to this the feature vector
Research Cell : An International Journal
ISSN: 2229-6913 (Print), ISSN: 2320-0332 (Online), Web Presence:
© 2016 Vidya Publications. Authors are responsible for any plagiarism issues.
can be computed using these techniques with high speed.
recognition are zoning, moments, projection histograms, crossings, character loci a
methods based on profiles, crossings, histograms, zoning have been mostly used as complementary or supporting
features to enhance the performance of primary features [14
combination of statistical and structural features for handwritten character recognition[3, 5, 9,14].
above said feature extraction methods, some more features are also available which have very good dis
ability. Some of these features give very good results and are based on gradient (
chain code histograms of image contour[26
image (binary or gray)[34-35], stroke based[33
character[32], directional distance distribution[29
Segmentation is one among the various pre
classification is carried out. The variability caused in
even to read a hand-printed material by a well knower of the language and so reading the same script through a
machine cannot be expected. In automatic script recognition, a machine is trained to recognize the scripts of a
particular language so that it can decide the meaning of a
known as machine learning. In machine recognition process the same strategy is followed. Therefore, there are three
ways to automate a system for word recognition: 1) Segmentation Based, 2) Hol
based approach, the given word is segregated into individual components and each component is recognized and
assigned a symbol, and the resulting symbols are reassembled to know the identity of a word. Whereas in holistic
approach, a word is not broken down into individual components rather it is trained with a complete set of words by
extracting their properties. Some segmentation techniques used in Indian languages are given in [
The various efforts done for the recognition of Devanagari are reported by
who done work for Devanagari script recognition prior to 1990 are
script of Devanagari after 1990 are:[23-26]. The va
Sharma et al[54], Deshpande et al [55] and Kumar
recognition are: Shaw et al[58], Parui et al[59
Hindi is widely used language in south Asian region which is written in Devanagari script. Hindi is also official
language of India. This research paper covers the various issues related to the recognition of
characters and words. Section 2 covers Devanagari script analysis. Section 3 covers various structural issues in
character recognition. Section 4 covers various structural issues in word recognition.
complexities of Devanagari Words. Section
discussion and conclusion is coved in Section 7.
2. Script Analysis
In case of Devanagari script based languages, the words are written lineother majority of languages of world. So segmenting a page into lines and then lines into words follow the same strategy as it is used for other languages. language-dependent. Among Hindi and Sanskrit which are written using Devanagari script, it is difficult to recognize Sanskrit as its words are longer and compodifficult to locate not only in hand-printed but in cursive manner but there are some alphabets which writing decides whether its expression is cursive or not.
Satish Kumar
Research Cell : An International Journal of Engineering Sciences, Issue June 2016, Vol. 18
0332 (Online), Web Presence: http://www.ijoes.vidyapublications.com
Authors are responsible for any plagiarism issues.
can be computed using these techniques with high speed. Some most commonly used statistical features for character
recognition are zoning, moments, projection histograms, crossings, character loci and n-tuple.
methods based on profiles, crossings, histograms, zoning have been mostly used as complementary or supporting
formance of primary features [14,16-18]. Some authors also have also used a
combination of statistical and structural features for handwritten character recognition[3, 5, 9,14].
above said feature extraction methods, some more features are also available which have very good dis
ability. Some of these features give very good results and are based on gradient ( Sobel or Prewit or Kirsch)[19
tograms of image contour[26-27], distance transform[28-29], pixel distance[30], a fixed size normalized
35], stroke based[33], feature based on foreground and backgroun
tional distance distribution[29] and directional element feature[31].
Segmentation is one among the various pre-processing steps performed on an image before
carried out. The variability caused in script writing is so high that sometimes
material by a well knower of the language and so reading the same script through a
In automatic script recognition, a machine is trained to recognize the scripts of a
particular language so that it can decide the meaning of a script under study. The process of training a machine is also
known as machine learning. In machine recognition process the same strategy is followed. Therefore, there are three
ways to automate a system for word recognition: 1) Segmentation Based, 2) Holistic, 3). Hybrid. In segmentation
based approach, the given word is segregated into individual components and each component is recognized and
assigned a symbol, and the resulting symbols are reassembled to know the identity of a word. Whereas in holistic
approach, a word is not broken down into individual components rather it is trained with a complete set of words by
Some segmentation techniques used in Indian languages are given in [
recognition of Devanagari are reported by Ghosh[64] and Pal [6
Devanagari script recognition prior to 1990 are :[47,50-52]. The major studies on machine
26]. The various studies on Devanagari character recognition are
and Kumar [56-57],. The various studies on hand-printed
Parui et al[59], Ramteke et al[61], Jayadevan et al[62], Oval[63]
Hindi is widely used language in south Asian region which is written in Devanagari script. Hindi is also official
This research paper covers the various issues related to the recognition of
and words. Section 2 covers Devanagari script analysis. Section 3 covers various structural issues in
character recognition. Section 4 covers various structural issues in word recognition. Section
Section 6 covers issues arising due to skeletonization of
discussion and conclusion is coved in Section 7.
In case of Devanagari script based languages, the words are written line-by line from top to bottom and left to right like languages of world. So segmenting a page into lines and then lines into words follow the same d for other languages. But segmentation technique required to segment a word into characters is
dependent. Among Hindi and Sanskrit which are written using Devanagari script, it is difficult to recognize Sanskrit as its words are longer and composed of a large number of half characters and / or vowels (matra) which are
printed but in machine printed too. The words in Devanagari script are not written in cursive manner but there are some alphabets which are written in cursive manner. Further, the style of individual writing decides whether its expression is cursive or not. In machine printed, variation in representation depends upon
179
Some most commonly used statistical features for character
tuple. The feature extraction
methods based on profiles, crossings, histograms, zoning have been mostly used as complementary or supporting
Some authors also have also used a
combination of statistical and structural features for handwritten character recognition[3, 5, 9,14]. In addition to
above said feature extraction methods, some more features are also available which have very good discrimination
Sobel or Prewit or Kirsch)[19-25] ,
], a fixed size normalized
], feature based on foreground and background information of a
erformed on an image before feature extraction and
sometimes it becomes difficult
material by a well knower of the language and so reading the same script through a
In automatic script recognition, a machine is trained to recognize the scripts of a
script under study. The process of training a machine is also
known as machine learning. In machine recognition process the same strategy is followed. Therefore, there are three
istic, 3). Hybrid. In segmentation
based approach, the given word is segregated into individual components and each component is recognized and
assigned a symbol, and the resulting symbols are reassembled to know the identity of a word. Whereas in holistic
approach, a word is not broken down into individual components rather it is trained with a complete set of words by
Some segmentation techniques used in Indian languages are given in [36-45].
Pal [65]. The various authors
]. The major studies on machine-printed
rious studies on Devanagari character recognition are: Pal et al[53],
printed Devanagari word
3] and Singh[60].
Hindi is widely used language in south Asian region which is written in Devanagari script. Hindi is also official
This research paper covers the various issues related to the recognition of Devanagari hand-printed
and words. Section 2 covers Devanagari script analysis. Section 3 covers various structural issues in
Section 5 covers recognition
of word level images. The
by line from top to bottom and left to right like languages of world. So segmenting a page into lines and then lines into words follow the same
But segmentation technique required to segment a word into characters is dependent. Among Hindi and Sanskrit which are written using Devanagari script, it is difficult to recognize
sed of a large number of half characters and / or vowels (matra) which are The words in Devanagari script are not written
Further, the style of individual In machine printed, variation in representation depends upon
Research Cell : An International Journal
ISSN: 2229-6913 (Print), ISSN: 2320-0332 (Online), Web Presence:
© 2016 Vidya Publications. Authors are responsible for any plagiarism issues.
the type-set of a font but in case of hand-print there is no limit.along their position in a word is given in T
Table 1: Devanagari Alphabet set along with their position in a word.
Like other languages, in Devanagari script too, a word is vertically divided into three regions: the upper region, the
middle region and the lower region. The upper region is occupied by the vowels or the part of vowels; the lower
region is also occupied by the vowels while the
compound characters, and a vowel (matra
three vertical regions is given in Fig 1.
Fig 1: Devanagari word divided into three vertical regions.
Some symbols mentioned as “Non Touching with Head
where as in some cases lower part of middle region is a little bit empty. The above said fact
machine–printed. Some full size, head
words in machine-printed form are depicted
Fig 2: The alphabets of various
3. Structural Issues in Character Recognition
Region
Upper
Middle full
Touching with
(Occupy upper part only)
Non-Touching with Head
Lower
Satish Kumar
Research Cell : An International Journal of Engineering Sciences, Issue June 2016, Vol. 18
0332 (Online), Web Presence: http://www.ijoes.vidyapublications.com
Authors are responsible for any plagiarism issues.
print there is no limit. The basic Devanagari alphabetis given in Table 1.
Table 1: Devanagari Alphabet set along with their position in a word.
n Devanagari script too, a word is vertically divided into three regions: the upper region, the
middle region and the lower region. The upper region is occupied by the vowels or the part of vowels; the lower
region is also occupied by the vowels while the middle region is occupied by the vowels, consonants, pure consonants,
matra) or a part of matra. A hand-printed Devanagari word demonstrating all the
Devanagari word divided into three vertical regions.
Touching with Head-line” in Table 1 cover whole lower part of middle region
where as in some cases lower part of middle region is a little bit empty. The above said fact
ome full size, head-line touching and non head-line touching characters in some Devanagari
printed form are depicted in Fig 2.
The alphabets of various categories Devanagari words.
in Character Recognition
Symbols
Touching with Head-line
(Occupy upper part only)
Touching with Head-line
180
Devanagari alphabets generally used in Hindi
Table 1: Devanagari Alphabet set along with their position in a word.
n Devanagari script too, a word is vertically divided into three regions: the upper region, the
middle region and the lower region. The upper region is occupied by the vowels or the part of vowels; the lower
middle region is occupied by the vowels, consonants, pure consonants,
printed Devanagari word demonstrating all the
line” in Table 1 cover whole lower part of middle region
where as in some cases lower part of middle region is a little bit empty. The above said fact is true only for the
line touching characters in some Devanagari
Research Cell : An International Journal
ISSN: 2229-6913 (Print), ISSN: 2320-0332 (Online), Web Presence:
© 2016 Vidya Publications. Authors are responsible for any plagiarism issues.
Structural features are based on the geometrical and topological properties of local or global [1]. A character is composed of number of components in the form of strokes. lines, arcs, curves, etc. Each character component is called as a character primitive. These are extracted either from skeleton or contour of a character image. The various stroke primitives are extracted and approximated. The relationship between these components is established. The character is recognized on the basis of number of such components, the kinds of components and the relationship between these components in some order. The various structural methods differ in respect of primitive selection and their association for shape depiction. Some commonly used topological and geometrical features in character recognition are: endpoints, Tbottom, left and right), direction of stroke, loops, convepoints, etc.
3.1 Drawbacks of Structural Features for Characters Recognition:
approaches [5, 16] and these are as:
1). Since the number of primitives present in a character image is not known prior, it is very difficult to detect the
primitive and estimate its features. Therefore, the success of applying structural feature depends upon the prior
knowledge of shape boundary features stored in a databas
2). The matching schemes used in structural approaches use non
guarantee best match.
3). The structural features are sensitive to noise and this representation do not preserve the topological structure of
object. With change in boundary of an image the local features change a lot.
4). In structural based approach, though the character primitives provide a stable representation but it does not
completely cover the variability in characters. The
variable writing styles is not easy to handle in classification stage.
Moreover, the structural features are extracted from a skeleton. Thinning process introduces spurious branches and
clusters as given in Fig 3. These defects are common in handwritten character images. The spurious branches and
clusters of small size are easy to remove but bigger size poses difficulty.
Fig 3: Spurious branches and clusters created due to thinning process.
Even performing primary classification based on topological features in Devanagari handwritten character recognition
is difficult. For example, if we want to categorize Devanagari alphabet set into subsets on the basis of number of end
points. It is very difficult as the number of end points of a character is different corresponding to different writing. The
various intra-class Devanagari handwritten characters with varying number of en
characters are taken after head-line removal.
Satish Kumar
Research Cell : An International Journal of Engineering Sciences, Issue June 2016, Vol. 18
0332 (Online), Web Presence: http://www.ijoes.vidyapublications.com
Authors are responsible for any plagiarism issues.
Structural features are based on the geometrical and topological properties of a character and these prop]. A character is composed of number of components in the form of strokes.
lines, arcs, curves, etc. Each character component is called as a character primitive. These are extracted either from skeleton or contour of a character image. The various stroke primitives are extracted and approximated. The
ship between these components is established. The character is recognized on the basis of number of such components, the kinds of components and the relationship between these components in some order. The various
rimitive selection and their association for shape depiction. Some commonly used topological and geometrical features in character recognition are: endpoints, T-points, cross points, extrema (top, bottom, left and right), direction of stroke, loops, convex and concave arcs, straight lines, directi
Drawbacks of Structural Features for Characters Recognition: There are some drawbacks of
s present in a character image is not known prior, it is very difficult to detect the
primitive and estimate its features. Therefore, the success of applying structural feature depends upon the prior
knowledge of shape boundary features stored in a database.
he matching schemes used in structural approaches use non-metric similarity measure. These methods do not
he structural features are sensitive to noise and this representation do not preserve the topological structure of
object. With change in boundary of an image the local features change a lot.
n structural based approach, though the character primitives provide a stable representation but it does not
completely cover the variability in characters. The instability caused in feature space due to incomplete recovery of
variable writing styles is not easy to handle in classification stage.
Moreover, the structural features are extracted from a skeleton. Thinning process introduces spurious branches and
. These defects are common in handwritten character images. The spurious branches and
clusters of small size are easy to remove but bigger size poses difficulty.
: Spurious branches and clusters created due to thinning process.
Even performing primary classification based on topological features in Devanagari handwritten character recognition
is difficult. For example, if we want to categorize Devanagari alphabet set into subsets on the basis of number of end
difficult as the number of end points of a character is different corresponding to different writing. The
class Devanagari handwritten characters with varying number of end points are given in Fig 4
line removal.
181
and these properties may be ]. A character is composed of number of components in the form of strokes. These strokes may be
lines, arcs, curves, etc. Each character component is called as a character primitive. These are extracted either from skeleton or contour of a character image. The various stroke primitives are extracted and approximated. The
ship between these components is established. The character is recognized on the basis of number of such components, the kinds of components and the relationship between these components in some order. The various
rimitive selection and their association for shape depiction. Some commonly points, cross points, extrema (top,
x and concave arcs, straight lines, directional points, bend
There are some drawbacks of using structural
s present in a character image is not known prior, it is very difficult to detect the
primitive and estimate its features. Therefore, the success of applying structural feature depends upon the prior
metric similarity measure. These methods do not
he structural features are sensitive to noise and this representation do not preserve the topological structure of an
n structural based approach, though the character primitives provide a stable representation but it does not
instability caused in feature space due to incomplete recovery of
Moreover, the structural features are extracted from a skeleton. Thinning process introduces spurious branches and
. These defects are common in handwritten character images. The spurious branches and
: Spurious branches and clusters created due to thinning process.
Even performing primary classification based on topological features in Devanagari handwritten character recognition
is difficult. For example, if we want to categorize Devanagari alphabet set into subsets on the basis of number of end
difficult as the number of end points of a character is different corresponding to different writing. The
d points are given in Fig 4. All these
Research Cell : An International Journal
ISSN: 2229-6913 (Print), ISSN: 2320-0332 (Online), Web Presence:
© 2016 Vidya Publications. Authors are responsible for any plagiarism issues.
Fig 4: Some Devanagari handwritten characters with varying number of end points.
Some more reasons for not using structural features for Devanagari handwritten character recognition are as
follows[46]:
1). Extracting structural features are very difficult
a) Due to complex structure of some of its characters.
b) Intra-touching (mingle) of the various strokes present in a character
style of writing or ink blot.
Fig 5: Some Devanagari characters with
c) Some characters are built up from multi
character, Fig 6, due to hasty writing or individual style of writing.
Fig 6: Multi
d) There is blurring present in some characters. Extracting skeleton from blurred image destroy its shape
demonstrates the situation, where
Fig 7: Blurred
Satish Kumar
Research Cell : An International Journal of Engineering Sciences, Issue June 2016, Vol. 18
0332 (Online), Web Presence: http://www.ijoes.vidyapublications.com
Authors are responsible for any plagiarism issues.
: Some Devanagari handwritten characters with varying number of end points.
Some more reasons for not using structural features for Devanagari handwritten character recognition are as
are very difficult
) Due to complex structure of some of its characters.
various strokes present in a character, Fig 5, due to hasty
: Some Devanagari characters with intra-touching strokes.
) Some characters are built up from multi-strokes and these primitive may not be touching as it is required in a
due to hasty writing or individual style of writing.
ulti-stroke characters with dissociated primitives.
There is blurring present in some characters. Extracting skeleton from blurred image destroy its shape
, where encircled parts of the characters are completely vanished
: Blurred (encircled) and vanished Devanagari characters
182
: Some Devanagari handwritten characters with varying number of end points.
Some more reasons for not using structural features for Devanagari handwritten character recognition are as
hasty writing or individual
strokes and these primitive may not be touching as it is required in a
There is blurring present in some characters. Extracting skeleton from blurred image destroy its shape. Fig 7
vanished.
Research Cell : An International Journal
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2). It is difficult to extract structural features from noisy images as small loops are created in skeletonization process.
3). Extra branches are created due to fluent
skeletonized image of a character. These extra branches become more apparent after skeltonization of a character
image is carried. Such unpredictable behavior is common
Fig
4. Structural Issues in Word Recognition
In segmentation based approach, a given word is segregated into individual components and each component is
recognized and assigned a symbol, and the resulting symbols are reassembled to know the identity of a word.
printed writing the variability caused is so high that
regions as the size of different characters varies due to the lack of control while writing. The
demonstrates this situation. As a head-line exists in case of words / characters written in D
to locate boundary between upper and middle regions and helpful in segregating /
region. It is very difficult to locate the boundary between
Fig 9: Some full size, head-line touching and non
Though some characters of Devanagari script are cursive in writing but while writing a word each character is written
individually. A Devanagari word is formed by connecting its letters with each other through head
character in a word may touch with adjoining character either due to hasty writing or due to ink stains.
Fig 10: A Hindi word
In case of Devanagari, some vowels are formed with the symbols those occupy both upper and middle
recognition process, it is important to decide about a vowel by considering the various parts of a symbol presented
both in upper and lower regions which are some what difficult to locate in the sense that they may be written / placed
in a word deviating from the rules. The recognition process may produce the output of such image as ambiguous word.
The possible word images with exact location of vowels for the word image demonstrated in
with minor change in location of symbols in upper or middle creates a new word as given in Fig 10(right).
Satish Kumar
Research Cell : An International Journal of Engineering Sciences, Issue June 2016, Vol. 18
0332 (Online), Web Presence: http://www.ijoes.vidyapublications.com
Authors are responsible for any plagiarism issues.
2). It is difficult to extract structural features from noisy images as small loops are created in skeletonization process.
due to fluent writing as shown in Fig 8. Structural features are extracted from the
These extra branches become more apparent after skeltonization of a character
Such unpredictable behavior is common in skeletonizing algorithms [66].
8: Extra branches produced due to writing.
Recognition
given word is segregated into individual components and each component is
recognized and assigned a symbol, and the resulting symbols are reassembled to know the identity of a word.
is so high that it is very difficult to locate boundary between middle and lower
regions as the size of different characters varies due to the lack of control while writing. The
line exists in case of words / characters written in Devanagari script, it is
er and middle regions and helpful in segregating / locatin
region. It is very difficult to locate the boundary between middle and lower region.
line touching and non head-line touching characters in some Devanagari words in
hand-printed form.
Though some characters of Devanagari script are cursive in writing but while writing a word each character is written
A Devanagari word is formed by connecting its letters with each other through head
touch with adjoining character either due to hasty writing or due to ink stains.
word where matra not in its original position(right).
In case of Devanagari, some vowels are formed with the symbols those occupy both upper and middle
recognition process, it is important to decide about a vowel by considering the various parts of a symbol presented
both in upper and lower regions which are some what difficult to locate in the sense that they may be written / placed
The recognition process may produce the output of such image as ambiguous word.
The possible word images with exact location of vowels for the word image demonstrated in
mbols in upper or middle creates a new word as given in Fig 10(right).
183
2). It is difficult to extract structural features from noisy images as small loops are created in skeletonization process.
. Structural features are extracted from the
These extra branches become more apparent after skeltonization of a character
].
given word is segregated into individual components and each component is
recognized and assigned a symbol, and the resulting symbols are reassembled to know the identity of a word. In hand-
t is very difficult to locate boundary between middle and lower
regions as the size of different characters varies due to the lack of control while writing. The Fig 9 clearly
evanagari script, it is easy
locating the symbols of upper
line touching characters in some Devanagari words in
Though some characters of Devanagari script are cursive in writing but while writing a word each character is written
A Devanagari word is formed by connecting its letters with each other through head-line. However, a
touch with adjoining character either due to hasty writing or due to ink stains.
.
In case of Devanagari, some vowels are formed with the symbols those occupy both upper and middle regions. In
recognition process, it is important to decide about a vowel by considering the various parts of a symbol presented
both in upper and lower regions which are some what difficult to locate in the sense that they may be written / placed
The recognition process may produce the output of such image as ambiguous word.
The possible word images with exact location of vowels for the word image demonstrated in Fig 10(left). However,
mbols in upper or middle creates a new word as given in Fig 10(right).
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In addition to this, some symbols of Devanagari script are built up with multiple strokes which may be dissociated
from each other in a given word. In such cases decision about a chara
available stroke components. In Fig 11
difficulty in segmenting and recognizing such characters.
Fig 11: A word written in Devanagari script having broken character primitives
Among the various middle region full size symbols only ‘
vowel and remaining symbols are either consonants or pure consona
middle region, Fig 12, may create confusion with noise present in an image.
frequently used vowel in Devanagari wher
Fig 12: A matra or
In Hindi it is scarcely used and in Sanskrit it
demonstrated in Fig 12. The use of visarga is very less in middle of a word where its use at end of word is common
in Sanskrit. Like Roman, “-” (hyphen) is also used in Devanagari to join two words. This also lies in middle region
but its presence is outside a word. For recognition purpose, it also needs to be treated as
5. Recognition Complexities of Devanagari Words
There are three kinds of approaches to recognize the words of any language
(holistic) and hybrid. The segmentation free approach, as already mentioned, requires a big lexicon of words which
needs a lot of efforts for lexicon creation. The segmentation
segmentation process in it. Some issues required to be dealt with the recognition of words pertaining to Devanagari
scripts using this approach are as follows:
1) There are two ways to segregate Devanagari words into characters and then recognize them individually
either after removing head-line or without removing head
box characters\matra chopped off due to which it poses a problem to recognize
present in a word. How the head-line f
various characters in a given word image?
2) If a word is segmented without removing head
character in a word which is non-linear.
characters even if two adjoining character
Satish Kumar
Research Cell : An International Journal of Engineering Sciences, Issue June 2016, Vol. 18
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Authors are responsible for any plagiarism issues.
In addition to this, some symbols of Devanagari script are built up with multiple strokes which may be dissociated
from each other in a given word. In such cases decision about a character must be made keeping in view the various
ble stroke components. In Fig 11, are in broken form and there may be
difficulty in segmenting and recognizing such characters.
A word written in Devanagari script having broken character primitives
Among the various middle region full size symbols only ‘ ’ (kanna) and ‘:’ (visarga) belong to vowel or a part of
vowel and remaining symbols are either consonants or pure consonants or compound characters.
may create confusion with noise present in an image. The symbol
frequently used vowel in Devanagari where as the use of symbol ‘:’ (visarg) is language dependen
A matra or any other symbol in middle region.
In Hindi it is scarcely used and in Sanskrit it is frequently used. The use of ‘ ’ and ‘:’ in middle
The use of visarga is very less in middle of a word where its use at end of word is common
” (hyphen) is also used in Devanagari to join two words. This also lies in middle region
For recognition purpose, it also needs to be treated as a special symbol.
Recognition Complexities of Devanagari Words
There are three kinds of approaches to recognize the words of any language i.e. segmentation based, segmentation free
The segmentation free approach, as already mentioned, requires a big lexicon of words which
needs a lot of efforts for lexicon creation. The segmentation based approach is easy but it is
ssues required to be dealt with the recognition of words pertaining to Devanagari
as follows:
There are two ways to segregate Devanagari words into characters and then recognize them individually
line or without removing head-line. While removing head-line some parts of middle
chopped off due to which it poses a problem to recognize various individual
line from a word may be removed without chopping off important parts of
various characters in a given word image?
If a word is segmented without removing head-line, it is difficult to estimate the left and right boundary of a
near. Also it becomes difficult to decide the dissociati
characters are not fused.
184
In addition to this, some symbols of Devanagari script are built up with multiple strokes which may be dissociated
cter must be made keeping in view the various
are in broken form and there may be
A word written in Devanagari script having broken character primitives
) belong to vowel or a part of
nts or compound characters. Visarga lies in
The symbol ‘ ’ (kanna ) is most
rg) is language dependent.
’ and ‘:’ in middle region is
The use of visarga is very less in middle of a word where its use at end of word is common
” (hyphen) is also used in Devanagari to join two words. This also lies in middle region
special symbol.
segmentation based, segmentation free
The segmentation free approach, as already mentioned, requires a big lexicon of words which
it is difficult to carry out
ssues required to be dealt with the recognition of words pertaining to Devanagari
There are two ways to segregate Devanagari words into characters and then recognize them individually i.e.
line some parts of middle
various individual characters
rom a word may be removed without chopping off important parts of
line, it is difficult to estimate the left and right boundary of a
Also it becomes difficult to decide the dissociation point between two
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3) Due to hasty writing, the two adjoining characters may
touching characters?
4) Some multi-stroke characters, Fig 1
those components are recognized to take decision about the exact/ tentative cha
5) There may be a vowel attached to a character on its lower part.
the presence/ absence of a vowel on the lower part of a character?
6) There are some vowels in Devanagari,
regions. How to locate and recognize such vowels?
7) Some pure consonants (half characters) presented inside a word may form a complex structure by combining
with other characters. Some such complex structures formed due to the merging of a consonant and a pure
consonant are demonstrated inside dotted
components?
Fig 13: Some complex structures formed due to the merging o
8) Various matra attached on lower or upper part of a character may be bigger in size due to which it encroach
the areas of the adjoining character
equivalent to characters size to which it is attached.
aggregates the ambiguity about final word recognition.
9) Various complex compound characters are formed due to merge of two or more characters.
characters are written left to right, the fusion is expected
but this is not only the case in
inside dotted rectangle, making it difficult to know whether to dissociate
horizontally or vertically.
Fig 14 (
Satish Kumar
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two adjoining characters may touch each other. How to locate the boundary between
, Fig 11, in a word may not be connected to each other as these
those components are recognized to take decision about the exact/ tentative character?
There may be a vowel attached to a character on its lower part. Fig 9 demonstrates the situation.
the presence/ absence of a vowel on the lower part of a character?
There are some vowels in Devanagari, Fig 10(right), which may be mis-positioned both in upper
regions. How to locate and recognize such vowels?
Some pure consonants (half characters) presented inside a word may form a complex structure by combining
Some such complex structures formed due to the merging of a consonant and a pure
consonant are demonstrated inside dotted rectangle areas in Fig 13. How to locate and recognize such
Some complex structures formed due to the merging of consonants and pure consonants.
attached on lower or upper part of a character may be bigger in size due to which it encroach
the areas of the adjoining characters. In machine printed the size of such matra or a part of matra
equivalent to characters size to which it is attached. Such matra may create confusion
aggregates the ambiguity about final word recognition. Fig 14 depicts the situation.
Fig 14: Confused location of lower Matra.
Various complex compound characters are formed due to merge of two or more characters.
ten left to right, the fusion is expected horizontal as given in Fig 14(a), inside dotted circle,
in Devanagari where the fusion also occurs vertically as given in Fig 14(a
making it difficult to know whether to dissociate such fused
Fig 14 (a): Horizontal and vertically fused characters.
185
each other. How to locate the boundary between
connected to each other as these should be. How
the situation. How to decide
positioned both in upper or middle
Some pure consonants (half characters) presented inside a word may form a complex structure by combining
Some such complex structures formed due to the merging of a consonant and a pure
How to locate and recognize such
f consonants and pure consonants.
attached on lower or upper part of a character may be bigger in size due to which it encroach
the size of such matra or a part of matra remains
confusion about its location. This
Various complex compound characters are formed due to merge of two or more characters. Since, the
horizontal as given in Fig 14(a), inside dotted circle,
vertically as given in Fig 14(a) ,
such fused compound characters
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10) Some compound characters, built up due to combine of two
symbol in Devanagari as given in Fig 14(b).
complexity for recognition. The numbers of such characters
Fig 14 (
11) Some awkward styles of writing such as undeveloped, unusual and extra
Devanagari word recognition. In addition some irregularities committed by the writers while writing further
adds complicacies in Devanagari word recognition problem
6. Word Level Skeletonization or Contouring
A word is composed of number of characters each of which is formed from a number of stroke primitives. These
strokes may be lines, arcs, curves, etc and may or may not be connected to each other depending upon the structure
of a character. The stroke primitives can be extracted from eith
structural based recognition process, the various stroke primitives of a character are extracted and approximated.
The relationships between various stroke components are established. Some difficulties in rec
pertaining to Devanagari script using structural approach
word are as follows:
1). A Devanagari word consists of a head
produced is not smooth which is difficult to remove. One such
given in Fig 15 mentioned using dotted
2). When a word is segmented after skeletonization, the two characters which are fused may form a cluster or
multiple joint which is difficult to segment. The situation is given in
3). In segmentation based approach, each segmented character/ part of character is matched against some already
known characters. If a character is in skeleton form, then the skeletonization process may have lost important
part of a character image or may have created extra
character image etc., which complicates the recognition process a lot.
Fig 15: Skeletonized image of hand
4). The various character level recognition 3.1 further adds to word level recognition
Satish Kumar
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ome compound characters, built up due to combine of two characters, are also represented using special
symbol in Devanagari as given in Fig 14(b). These symbols form a complex structure and
The numbers of such characters are more in Sanskrit than Hindi.
Fig 14 (b): Special symbol for fused characters.
Some awkward styles of writing such as undeveloped, unusual and extra stroke writing
In addition some irregularities committed by the writers while writing further
Devanagari word recognition problem[67].
or Contouring Issues
characters each of which is formed from a number of stroke primitives. These
strokes may be lines, arcs, curves, etc and may or may not be connected to each other depending upon the structure
of a character. The stroke primitives can be extracted from either skeleton or contour of a character image. In
structural based recognition process, the various stroke primitives of a character are extracted and approximated.
The relationships between various stroke components are established. Some difficulties in rec
pertaining to Devanagari script using structural approach which is based on skeletonized
A Devanagari word consists of a head-line. When the given word image is skeletonized, the resultant head
produced is not smooth which is difficult to remove. One such irregular structure produced in
mentioned using dotted rectangle area.
When a word is segmented after skeletonization, the two characters which are fused may form a cluster or
multiple joint which is difficult to segment. The situation is given in Fig 15 mentioned using dotted circles.
based approach, each segmented character/ part of character is matched against some already
known characters. If a character is in skeleton form, then the skeletonization process may have lost important
part of a character image or may have created extra parts in a character image or may have created clusters in a
character image etc., which complicates the recognition process a lot.
Skeletonized image of hand-printed Devanagari word image
The various character level recognition problems based on skeletonized character images mentioned in Section recognition.
186
represented using special
a complex structure and are a source of
more in Sanskrit than Hindi.
stroke writing also pose problems in
In addition some irregularities committed by the writers while writing further
characters each of which is formed from a number of stroke primitives. These
strokes may be lines, arcs, curves, etc and may or may not be connected to each other depending upon the structure
er skeleton or contour of a character image. In
structural based recognition process, the various stroke primitives of a character are extracted and approximated.
The relationships between various stroke components are established. Some difficulties in recognizing words
skeletonized or contoured image of a
line. When the given word image is skeletonized, the resultant head-line
irregular structure produced in head-line is
When a word is segmented after skeletonization, the two characters which are fused may form a cluster or
mentioned using dotted circles.
based approach, each segmented character/ part of character is matched against some already
known characters. If a character is in skeleton form, then the skeletonization process may have lost important
parts in a character image or may have created clusters in a
ri word image.
er images mentioned in Section
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5). During recognition process, the character images are normalized so that proper mapping between stored image and tested images is done. But itnormalization.
6). On the other hand, if a character\word structure is compared from the problems. The contoured image of a wordboundary where extra stokes of a character are generated
Fig 16: The co
7). Some unwanted complex structures are formarea, due to which segmentation or comparison becomes difficult.
Fig 17: The co
8). Moreover, if character\word strokes do not contain adequate size due to poor scanning, thin writing instrument tip, resizing or other reason, the contoured image of such characterresultant image becomes the mixer of contour and skeleton.
Fig 18: The skeleton strokes produced, dotted circles, during contouring.
Satish Kumar
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0332 (Online), Web Presence: http://www.ijoes.vidyapublications.com
Authors are responsible for any plagiarism issues.
recognition process, the character images are normalized so that proper mapping between stored image But it is difficult to decide that whether first skeletonization is performe
word structure is compared from the contoured imagetoured image of a word, Fig 9, is given in Fig 16. An unwanted structure (lump
boundary where extra stokes of a character are generated or ink blot occurs on boundary during writing process.
The contoured image of a word given in Fig 9.
complex structures are formed on fusion point of two or more characters, Figsegmentation or comparison becomes difficult.
The contoured image of a word given in Fig 13.
kes do not contain adequate size due to poor scanning, thin writing instrument tip, resizing or other reason, the contoured image of such character\word produce a skeleton as given in Fig 18. resultant image becomes the mixer of contour and skeleton. This also poses difficulty in comparison.
The skeleton strokes produced, dotted circles, during contouring.
187
recognition process, the character images are normalized so that proper mapping between stored image is difficult to decide that whether first skeletonization is performed or
contoured image then it has its own An unwanted structure (lump) is create on
during writing process.
of two or more characters, Fig 17 doted circular
kes do not contain adequate size due to poor scanning, thin writing instrument tip, word produce a skeleton as given in Fig 18. The
This also poses difficulty in comparison.
The skeleton strokes produced, dotted circles, during contouring.
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9). The images resized with normalization process also, sometimes, destroys strokes present in a character/word image due to which it becomes difficult to extract contour.
Some above mentioned issues are not only scripts.
7. Discussion and Conclusion
Though a lot of work has been done for the development of recognition system for the various languages of the world
but good ICR of majority of languages are still not available. Among the various Indian scri
script. The words of Devanagari script are formed by connecting various characters using head
presence of head-line it is easy to separate the symbols
becomes difficult to separate the symbols between
boundary. Though its words are not cursively written but some its characters are cursive i
has its own kinds of problems in respect of recognition. We have
of Devanagari hand-printed based on structural features.
structural features which are extracted from skeltonized
related to structural features also discussed.
discussed.
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Research Cell : An International Journal of Engineering Sciences, Issue June 2016, Vol. 18
0332 (Online), Web Presence: http://www.ijoes.vidyapublications.com
Authors are responsible for any plagiarism issues.
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