online digital signature recognition using kernel based ...joics.org/gallery/ics-1282.pdf ·...

Online Digital Signature Recognition Using Kernel based

SOM Method to enhance its performance and Accuracy

R. Ravi Chakravarthi

Research Scholar in Computer Technology,

Manonmaniam Sundaranar University,

Tirunelveli-627 012, Tamil Nadu, India

Dr. E. Chandra

Professor & Head of the Computer Science Department,

Bharathiar University, Coimbatore - 641046

Tamil Nadu. India

Abstract:

Signature is the world’s most popular authentication function which consists of one’s

own initials which differentiate their identity from others. It’s a unique way of identifying

individuals because each one in this world has a different kind of writing. Every two

signatures in the world differ from each other in terms of the individual's pressure given

while writing the language, the shape of the loops in the signature, the speed at which they

write, and limitless alternate selections. The unique features used in these signatures are

identified by using different algorithms to extract the unique options of each set and classified

using different classification techniques. OCR is the electronic conversion of handwritten

signatures into machine-encoded texts gathered from various sources. The character is

identified through various techniques such as division, highlight extraction, and

arrangement. The various techniques used in the OCR frameworks is optical checking, area

division, preprocessing, division, portrayal, highlight extraction, preparing, and

acknowledgment, and goes on. The clustering of input vectors id done by using a Kernel Self-

Organizing Feature Map (SOM) which uses a neural network for classification and trained

using unsupervised learning. This research focuses on using the Kernel SOM neural network

model to depict the signature verification. A Triple Crown implementation is implied for the

automatic biometric system which depends upon the training set consistency. Similar

signature’s which has a comparable author square measurement, but not identical to each

other disagrees in terms of location, scale and orientation both locally and globally.

Although there is a distinctive fingerprint, it is proved that there is no single square measure

for a signature. This paper insists on applying the competitive neural specification for

checking the information set consistency in a massive private biometric information set

obtained. A neural network is used to validate the consistency of the intra-variability of the

people signatures. A novel replacement democratic neural specification is used to minimize

the rejection error and maximize the share of correct classification for the renowned options

and replacement feature set.

Keywords: K-nearest neighbor’s algorithm, Optical Character Recognition, Kernel Self-

Organizing Map, Image segmentation, feature extraction, and classification.

Journal of Information and Computational Science

Volume 9 Issue 8 - 2019

ISSN: 1548-7741

www.joics.org869

I. Introduction

OCR is an automatic identification scheme which relies on machine identification

methods.

The programmedrecognitionmethod is a data capturing technique used to automatically

identify objects, gather knowledge from them, and store the gathered knowledge into a

computer system without any manual intervention. The external knowledge gathered is from

analyzing a series of images, audios or videos. An electrical device is used to convert the

input of images, audios or videos into a digitalreport. It is kept and analyzed for future

purposes. In order to detect and identify the text in the pictures and videos, an Automatic

Data Processing (ADP) system is developed. The ADP allows the system to mechanically

scan the text embedded deep within advanced backgrounds.

The final example can be illustrated using an associate example of identifying what

the ADP system is capable of doing. The ADP system should be always able to answer two

questions “Where? and What?”. The “Where” part determines where the text string is present

in the picture or video.The “What” part determines what the text string says about the picture

or video. In other words, the system mechanically identifies the individual text embedded in

advanced backgrounds and the necessary details such as each and every word in the text is

recognized. Various existingprogrammed recognition methods are reviewed and the OCR’s

location is calculated in between them and how superior is OCR than the techniques is also

calculated. An ordinary PC gains knowledge from the data entered from the keyboard. But

this method is not applicable in various cases. The automated identification network can

function in many cases which are categorized as important. This makes the ADP system

function in various application areas. OCR can be used in various applications. The

applications listed above is mainly used in the past eras[10] for that reason the applications

are not elaborated in details,its just used for understanding. (a) Speech recognition systems is a machine or program that able to recognize the phrases

and words in a spoken language. This language is converted into machine readable format. It

would recognize the voice using the algorithm.The voice is measured depends on the

accuracy and speed.

(b) This oftenness identification is act as a tagging system for the automatic frequency

identification of municipal solid waste stream in reusable elements. This radio frequency

identification technique was widely used by some various organization in different industrial

countries.

(c) The vision frameworks territory unit actualized through the used camera any place the

region of the item unit known by their structure or size. This methodology is generally used

in machines for the distribution of containers. Sort the sort of jug ought to be perceived as

introductory because the amount repaid for a jug relies upon its sort.

(d) The data contains in attractive stripes territory unit wide utilized on credit cards, and so on

a serious extraordinary measure of learning is kept on the attractive tape anyway

exceptionally planned scanners zone unit required and thus the data can't peruse by people.

(e) The Universal Product Code comprises of numerous dim and lightweight lines speaking

to code for partner 11 digit run, 10 of that decide genuine item. The Universal Product Code

is filtered optically once product transfersthrough glass window by aabsorbed beam of light



ISSN: 1548-7741

www.joics.org870

of frail force that is cleared crosswise over the glass window in an in all respects uniquely

structured checking design. The reflected light-weight is estimated and dissected by PC.

Because of early institutionalization scanner tags territory unit, these days wide utilized and

speak to a genuine portion of the general commercial center for programmed distinguishing

proof. The Universal Product Code speaks to a novel range that recognizes the product and a

value activity is essential to recover information concerning the worth. The twofold example

speaking to the Universal Product Code takes up a great deal of territory as the tiny quantity

of learning it truly contains. The scanner tags don't appear to be decipherable to people.

(f) The imprinting in ink is particularly utilized at interims stockpile appliance. The nature

zone unit filled up ink that coverssoftly diced attractive components. They arefilled up

counterfeit text styles that are explicitly intended for the applying. Before the characters zone

unit peruse the ink is presented to an attractive motion. This strategy highlights each

character and modifies the location. The characters zone unit peruse by translating the wave

structure got once checking the characters on a level plane. Each character is expected to

have its very own particular wave structure. Even though intended for machine perusing, the

characters region unit still neat to people. Nonetheless, perusing depends on the characters

being composed of ink.

(g) The optical imprint perusing innovation is utilized to enroll in the area of imprints. It's

acquainted with peruse shapes and place the information is given by stamping pre-

characterized options. Such structures are obvious to people. This methodology is affordable

once the info is influenced. It's predefined with a secured assortment of options.

OCR attempts to manage numerous issues with higher than referenced strategies for

programmed ID. They're required once the information is decipherable each to people and

machines. OCR frameworks have engraved an unmistakable section place in example

acknowledgment. Their peculiarity exists in the certain reality that it needn't bother with the

executives of a strategy that produces information. OCR manages the matter of perceiving

optically or printing has been finished while the net acknowledgment is accomplished where

pc identifies the characters as they're drawn. Each manually written and composed characters

could likewise be perceived anyway the exhibition is legitimately needy upon the standard of

information records. A great deal of unnatural the information is, higher is that the exhibition

of the OCR framework. Be that as it may, when it includes aggregates free penmanship

execution of OCR machines stays faulty.

Individual personality confirmation is a significant and every now and again essential

application. There are a few methodologies taken utilizing biometrics [5], [6], [8], [15].

Biometrics perceives an individual reliant on his way of life instead of what he has or what he

understands. That is completely masterminded to physical and direct biometrics. Physical

biometrics relies upon quick estimations of the physical parts, of the human body. Social

biometrics relies upon the estimation of a movement accomplished, by the individual [2]. An

imprint is perhaps the most prepared and most extensively recognized sort of individual

character affirmation, especially in genuine and fiscal issues.. The attributes of a mark can be

partitioned into 2 sorts; i.e., dynamicand static. Thedynamic highlights are those that are

covered up, for example, the weight connected at pen-tip, situation of pen-tip, and so forth.

Mark check procedures dependent on these qualities are alluded to as online mark

confirmation. Then again, static highlights the basic unmistakable highlights, for example,



ISSN: 1548-7741

www.joics.org871

the shape. Signature check strategies dependent on these qualities are alluded to as

disconnected mark confirmation. Since the static attributes are noticeable, it is feasible for a

talented falsifier to replicate these qualities. Since the dynamic attributes are not noticeable, it

isn't workable for any individual to duplicate these qualities as they join the essayist's close to

home style.

Programmed signature confirmation is fundamental because of the powerlessness to

recognize signature essentially by the visual investigation. Programmed assessment, however

not flawless, is very solid and effective. Impressive work has been done in the course of the

most recent couple of decades [9], [10], [12]. The most recent decade has realized an

expanded accentuation on utilization of neural systems for this reason [4], [14].

Here, we deliberate acknowledgment of separate dependent on social biometric, explicitly

over his mark. We think about the dynamic attributes of a mark and thus, utilize online

signature acknowledgment.

1.1 Goals

In the examination of content identification and acknowledgment in a convoluted

foundation is impelled by driving edge utilizations of the computerized interactive media

frameworks. These days extra and extra sound and visual data is caught, put away, conveyed

and oversaw in advanced structures. The wide utilization of advanced media records incites a

few new difficulties in versatile data obtaining an immense sight and sound framework for

the executives. Among the first exceptional are:

1. Automatic broadcast annotation: creates a structured, searchable read of archives of

the

broadcast content.

2. Digital media quality management: archives digital media files for economical media

management.

3. Video piece of writing and cataloging: catalogs video databases on basis of content

relevance;

4. Librarydigitizing:digitizescowlofjournals,magazinesandvariedvideosvictimizationadv

ancedimageandvideo OCR.

II. Proposed Method

A run of the mill OCR framework comprises of numerous components as appeared in

Fig. 1 [3, 7]. The digitize Analog archive exploitation an optical scanner in the essential

advance method. When areas containing content are settled each picture is extricated through

the division technique. The separated images are pre-prepared, taking out clamor to

encourage highlight extraction. The personality of every picture is initiate by correlation

separated alternatives with depictions of picture classes got over a past learning area. At last,

talk information is used to re-creation words and amounts of underlying content. These

means are in a matter of seconds gave here. Intrigued persuser will allude [11] for an extra

intricate discourse of OCR framework components.



ISSN: 1548-7741

www.joics.org872

2.1 Optical Scanning

In the main sectionof Optical Character Recognition is optical checking. Over the

filtering technique, an advanced picture of the first report is caught. In OCR optical scanners

are utilized that consolidate transport system and detecting gadget that changes over quality

into dark levels.

Fig 1: Flow chart of the proposed Technique

Composed records join dark print on a white foundation. When playing OCR

structure picture is recover into a bi-level high contrast picture. This technique alluded to as

thresholding is achieved on detector to abstain from squandering remember zone and

procedure exertion. The thresholding strategy is essential on grounds that the aftereffects of

acknowledgment are totally excited about the nature of the bi-level picture. A firm limit is

utilized any place dim levels beneath this edge are dark and levels over white. For top

refinement records with the uniform foundation, a pre-picked mounted limit is frequently

adequate. Be that as it may, reports experienced in pursue have rather enormous shifts. In

these cases, a great deal of refined systems for thresholding is expected to get savvy results.

The most straightforward thresholding techniques shift limit adjusting to local properties of

record like refinement and splendor. Be that as it may, such methodologies some of the time



ISSN: 1548-7741

www.joics.org873

depend upon structure filtering of the record which needs a ton of memory and procedure

capacity. The stream outline of the proposed strategy is demonstrated as above.

2.2 LOCATION SEGMENTATION

The following OCR portion is area division. Division decides the comprises of an

image. It's important to discover districts of the report that have composed learning and are

recognized by illustrations and figures. Such as, while action programmed email dealing with

wrapping locationhave to be set isolated from elective prints like stamps and friend’s logo’s,

before acknowledgment. When connected to content, division is the separation of characters

or words. The greater part of OCR calculations stage words into detached characters that are

perceived independently. Ordinarily division is performed by investigative each associated

part. This framework is clear to execute anyway issues emerge if characters bit or they're

divided and include numerous components. The most issues in division are: (a) unmistakable

commotion from the content (b) withdrawal of contacting and divided characters (c)

misconstruing designs and unadulterated science with content and a different way. For

intrigued perusers, extra subtleties are reachable in [11].

2.3 PREPROCESSING

Pre-processing is the 3rd section in Optical Character Recognition. The information

figuring on the data procurement kind is exposed to an assortment of fundamental procedure

ventures to make it usable inside the expressive phases of character investigation. The picture

following from the checking strategy may contain a specific amount of clamor. Retribution

on the scanner goals and in this manner the characteristic thresholding, characters could

likewise be filthy or cracked. Smoothing infers each fulfilling and weakening. Filling wipes

out little breaks, holes, and gaps in digitized characters whereas weakening diminishes the

component of the line. The principal regular strategy for smoothing moves a window over a

double picture of the character and applies sure standards to substance of the window. Pre-

preparing conjointly incorporates standardization related to smoothing. The standardization is

connected to get natures of unvaryingscale, inclination, revolution. The accurate pivot is

initiateover its edge. For turned pages and highlights of the content, variations of Hough

rebuild square measure usually utilized for police work slant.

The pre-preparing part in this way intends to create information that is simple for the

OCR frameworks to work precisely. It is a significant action to be achieved before real

information examination. The primary targets of pre-preparing can be directed as [1, 3]:

(a) Noise decrease (b) compressionand (c)normalization of the data.

(a) Noise reduction:

The commotion presented by optical examining gadget or composition

device causes detached line sections, knocks, and holes in lines, stuffed circles, and so

on. The mutilation together with local varieties, misreckoning of corners,

enlargement, and disintegration might be a potential downside. It's important to

dispense with these flaws before the genuine procedure of the data. The commotion

decrease methods are regularly grouped in 3 noteworthy sets [1, 3]: (i) noise modeling

(ii) morphological processes and (iii) filtering.



ISSN: 1548-7741

www.joics.org874

(i) Noise will more often than not be evacuated by movement procedures in the

event that it may have been feasible to demonstrate it. Nonetheless,

commotion demonstrating is beyond the realm of imagination in the greater

part of applications. There occurs some offered writing on commotion

displaying presented by optical twisting like dot, slant, and obscure. It's

conjointly feasible to survey the standard of the character pictures and expel

the clamor to a specific level [1, 3].

(ii) In the fundamental arrangement following the morphological tasks to channel

the character picture trade the difficulty in the activity by intelligent activities.

Various morphological activities are intended to append the messed upstrokes,

decay the associated strokes, smooth the shapes prune wild focuses, thin the

characters and concentrate the limits [1, 3]. The morphological activities are

with progress use to expel commotion on the character pictures because of the

nature of paper and ink besides as whimsical hand development.

(iii) Separating means to dispose of clamor and decrease deceptive focuses

ordinarily presented by poor rate of the data obtaining gadget and uneven

composition surface. Differed reflection and recurrence area channels have

been intended for this reason. The elementary arrangement is to tangle a pre-

characterized veil with picture to allot a cost to an image component as

perform of dim estimations of its adjacent pixels. Numerous channels are

intended for smoothing, honing, thresholding, evacuating somewhat finished

or hued foundation and refinement change capacities [1, 3].

(b) Compression:

It is outstanding that old style picture pressure methods change the picture

from space area to areas that are not reasonable for acknowledgment. The pressure for

OCR needs space area methods for saving shape data. The two prevalent pressure

methods utilized are: (i) thresholding and (ii) thinning.

(c) Normalization:

The institutionalization ways intend of dispose the varieties of the

composition and gain institutionalized information. Some of the unremarkably

utilized ways for institutionalization square measure [1, 3]: (i)

skew standardization and baseline extraction (ii) slant standardization (iii)

size standardization and (iv) Contour smoothing.

(i) Skew normalization and baseline extraction: owing to mistakes inside the

checking technique and expressive style the composing could likewise be

marginally inclining or arced at interim the picture. This may offended the

viability of calculations therefore should be recognized and remedied. For

sure, a few characters recognized in accordance with the relative position with

the importance the standard, the methods for benchmark extraction grasp

exploitation the projection profile of picture, closest neighbor bunch, cross-

connection procedure among lines and Hough revamp [1, 3]. An entirely

appalling closest neighbor is utilized for extricating standard of modern

penmanship in huge commotion [1, 3]. At the point when slant recognition the



ISSN: 1548-7741

www.joics.org875

character or word is meant the root, turned or extended till the benchmark is

level and retranslated into the visual presentation unit zone.

(ii) Slant normalization: One of the quantifiable elements in contrast hand-

composing structures is that the inclination edges between the longest strokes

in a word and in this manner the vertical heading. Inclination

institutionalization is utilized to standardize all characters to an ordinary sort.

The chief basic procedure for inclination estimation is that the count of the run

of the mill point of near vertical segments. The vertical line segments from the

shapes region unit separated by following chain code components utilizing a

join of 1dimensional channels [1, 3]. The directions of start and completion

purposes of every line component give inclination edge. The projection

profiles zone unit figured for an assortment of edges a long way from the

vertical heading [1, 3]. The edge equal to projection with best positive spinoff

is utilized to see the quantity measure of cover among vertical strokes and

accordingly overwhelming inclination edge. Inclination location is achieved

by partitioning the picture into vertical and flat windows [1, 3]. The inclination

is evaluated bolstered center of gravity of higher and lower 1/2 each window

found the middle value of over every one of windows. A variation of Hough

change is utilized through filtering left to directly over the picture and

calculative projections inside the heading of twenty-one very surprising

inclinations [1, 3]. The most elevated 3 projections for any inclination territory

unit extra and in this way the inclination with the most significant check is

taken due to the inclination cost. At times, the ubiquity frameworks don't

utilize incline amendment and remunerate it all through training stage [1, 3].

(iii) Size standardization is employed to regulate character size to a precise

customary. The OCR ways may utilize for every even and vertical size

standardization. The character is part of an assortment of regions and all of

those regions are one by one scaled [1, 3]. The measurement

institutionalization likewise can be executed as an area of the instructing stage

and in this way, the size parameters are measurable one by one for each

express training data [1, 3]. The example characters are nibbled by a bit

contracted to the ideal size that expands the prominence rate inside the training

data. The word acknowledgment jelly mammoth intra classification varieties

inside the length of words with the goal that they may moreover aid

acknowledgment; it will in general exclusively include vertical tallness

institutionalization or bases the level size institutionalization on size issue

determined for vertical institutionalization [1, 3].

(iv) Contour smoothing disposes of mistakes because of unpredictable hand

movement all through composition. It regularly decreases the quantity of test

focuses required to speak to the content thus expands intensity in enduring

preprocessing procedure [1, 3].

It is to be noticed that the above methods influence the information and may acquaint

sudden contortions with the character picture. Subsequently, these systems may cause the loss



ISSN: 1548-7741

www.joics.org876

of significant data about the composition and along these lines ought to be connected with

consideration.

2.4 SEGMENTATION

The preprocessing stage produces a perfect character picture inside the feeling that an

adequate amount of structured data, high pressure, and low commotion on a standardized

picture is acquired. Here character picture is metameric into its subcomponents. The division

is significant because of degree one will attain in the partition of varying lines inside the

characters straightforwardly influences the prominence rate. Interior division is utilized that

separates lines and bends inside the cursively composed characters. Albeit numerous striking

systems have created inside the past and scope of strategies have developed, the division of

cursive characters is partner degree unexplained disadvantage.

2.4.1 Representation

The fifth part of OCR is a portrayal. The picture portrayal plays one in all premier

essential jobs in any acknowledgment framework. Inside the most straightforward case, a

dark level or twofold pictures square measure nourished to a recognizer. Notwithstanding, in

the greater part of the acknowledgment frameworks in order to maintain a strategic distance

from further intricacy and to expand the precision of the calculations, an extra minimized and

trademark delineation is required. For this reason, a gathering of alternatives is removed for

each class that recognizes it from various classifications though staying invariant to

trademark varieties at interims the class [1, 3]. The character picture representation

techniques square measure normally classified into 3 noteworthy gatherings:

(a) international transformation and seriesenlargement(b) applied

mathematics illustration and (c) geometrical and topological statement.

(a) Series expansionand Global transformation: An everlasting sign, for the most part,

contains a ton of information at that point must be imagined for the point of order. This

may be valid for particular approximations of ceaseless flag too. A system to speak to an

image is by a straight mix of a progression of simpler well-plot capacities. The

coefficients of the straight blend offer smaller mystery composing called change or

arrangement development. Disfigurements like interpretation and revolutions square

measure invariant underneath worldwide change and arrangement development[1, 3].

(b) Statistical illustration: The portrayal of a natural picture through the association of

focuses deals with support varieties somewhat. Despite the fact that this sort of

representation doesn't empower the remaking of the main picture, it's utilized for

diminishing the element of the list of capabilities giving rapid and low multifaceted

nature. Some of the primary connected science alternatives utilized for character

delineation are: (i) division (ii) projections and (iii) crossings and distances.

(c) (c)topological andGeometrical depiction: The different worldwide and neighborhood

effects of characters can be tended to by geometrical and topological geographies with

outrageous adaptability to turns and style arrangements. This kind of portrayal may in like

way encode some getting some answers concerning the structure of an article or may give

a little information with respect to what kind of parts make up that object.

All in all, the real objective of portrayal is to extricate and choose a lot of highlights

that augments the acknowledgment rate with minimal measure of components. The



ISSN: 1548-7741

www.joics.org877

element extraction and choice are characterized [1, 3] as extricating the most delegate

data from the crude information which limits the inside class design fluctuation while

upgrading the between class design inconstancy.

2.5 Feature Extraction

In the Optical Character Recognition’s sixth component is including with the

extraction. The equitability of highlightingthe extraction also to catch the fundamental

qualities of images. Highlight extraction is acknowledged together of the principal

problematic issues of example acknowledgment. The principal undemanding methods for

depicting a character is by genuine arrangement picture. Another methodology is to remove

sure choices that describe images anyway leaves the immaterial qualities. The strategies for

the extraction of such choices region unit separated into 3 gatherings' viz. (a) conveyance of

focuses (b) changes and arrangement developments and (c) basic examination. The different

groups of alternatives territory units assessed predictable with their clamor affectability,

misshapen, simple usage, and use. The gauges utilized in this investigation are (a) quality

regarding commotion, contortions, vogue variety, interpretation and revolution and (b)

reasonable use as far as acknowledgment speed, execution quality, and autonomy. Some of

the normally utilized component extraction methods zone unit model coordinating and

connection, changes, appropriation of focuses and basic examination. For intrigued perusers

extra subtleties region unit out there in [11].

Another vital assignment identified with highlight extraction is the arrangement.

Characterization is the strategy for recognizing each character and conveyance to that right

character classification. The two essential classes of the grouping approach for the OCR

territory unit call from the earlier and auxiliary methodologies. In call, from the earlier

acknowledgment character portrayal is numerically depicted in the element vector. There

may also be design attributes gotten from the assortment of character that isn't as basically

measured. Here the connection between the attributes may important once choosing class

enrollment. for instance, on the off chance that we as a whole realize that a nature comprises

1 vertical & 1 even stroke, it will be either 'L' or 'T'. The connection into 2 strokes is expected

to differentiate the nature. The chief ways to deal with a call from the earlier

acknowledgment territory unit least separation classifiers, connected math classifiers, and

neural systems. In auxiliary acknowledgment syntactic techniques region unit, the chief

winning methodologies. a top to the bottom discourse of those methodologies is existing in

[3, 11].

2.6 Training and Recognition

In the 7th element of Optical Character Recogination is preparing the

acknowledgment. Optical Character Recognition frameworks broadly utilize the approach of

example acknowledgment which allots an obscure example into a predefined class. The four

common methodologies are examined in the OCR model as proposed in [1, 3]: (a) template

matching (b) Kernel SOM (c) structural methods and (d) statistical techniques. These

methodologies are neither essentially free nor incoherent from one another. Every so often,

an OCR method in one methodology can likewise be viewed as an individual from different

methodologies. In the majority of the above methodologies, OCR procedures utilize either



ISSN: 1548-7741

www.joics.org878

all-encompassing or systematic techniques for the preparation and acknowledgment stages.

The all-encompassing technique utilizes top-down methodologies for perceiving the deep

character killing the division issue. The cost for this operational sparing is to compel the issue

of OCR to constrained language. Likewise, because of the multifaceted nature presented by

the portrayal of a solitary character or stroke the acknowledgment precision is diminished.

Then again, the scientific systems utilize a base up methodology beginning from stroke or

character level and going toward creating an important content. The express or certain

division calculations are required for this procedure, adding additional multifaceted nature to

the issue as well as acquainting division blunders with the framework. Nonetheless, with the

collaboration of the division organizes, the issue is decreased to the acknowledgment of basic

disconnected characters or strokes, which can be taken care of for boundless terminology

with grand acknowledgment price.

Among these strategies, the Kernel SOM strategy gives better precision when

contrasted with others. The reference unit stores information for preparing while example

unit comprises information for approval. Here, smart choice unit utilizes a neural system

model; i.e, a Kernel SOM. It is an information perception method that decreases the

components of information using self-sorting out neural systems. Kernel SOM decreases high

dimensional information into a guide of generally 1 or 2 measurements by bunching

comparable information gatherings. This makes it simple to envision and arrange the

information. Kernel SOM neural system is made for the 7 individuals utilizing MATLAB. 4

highlights were removed from every one of 3 unique factors (p, x, and y). Thus, every mark

test is spoken to by an example Fkl of twelve highlights given by

Where k signifies the signer, l sample number, x, y andp the dynamic characteristics and n, a,

d and t the tokensfeatures;.Each signer delivers5 samples for training; these arejoint to

develop a 12 × 5 matrix Fkspecified by

Lastly, data from 7 signers is joint into asingle 12×35 matrix F, which is the reference

data, specified by

The Kernel SOM made before should be prepared to utilize the reference information F.

The length of the preparation relies upon the number of ages. One age of preparing is

characterized as a solitary introduction of all information vectors to the system. The

system is then refreshed by the consequences of every one of those introductions.

Preparing happens until the most extreme number of determined ages have been come to

or the exhibition objective is met. Here, the no of ages was controlled through

experimentation. As the no of pages expanded, rate mistake diminished. For more than



ISSN: 1548-7741

www.joics.org879

200 pages, the development was not noteworthy. In this manner, the preparation was

halted at 200 pages.

III. Experimental Results

The algorithm is executed on PC for evaluation purposes. The language used for

composing these projects is MATLAB.The step by step processing of the output image for

the proposed method is shown in figure 2.Figure 2 demonstrates each step in processing the

output image by distinguishing the various characteristics used and how they differ from each

other.

(a) Input Image (b) Resized image

(c ) Thinning Image (d) Output Image

Figure 2: Step by step output for the proposed method

This area analyzes the experimental results and testing done by the Kernel

SOM system for the sample data. The functioningof the proposed

signconfirmationscheme is validated using 2 types of error rates: FRR and FAR. FRR

is also known as Type I error which calculates the percentage rate of rejected genuine

signatures.FAR is also known as Type II Error,which calculates the percentage rate of

acceptance of fakes. The error rates for the proposed Kernel SOM system is evaluated

for calculating the system’s performance for 50 epochs and 200 epochs and the results

are tabulated in Table 1.



ISSN: 1548-7741

www.joics.org880

TABLE I

SYSTEM PERFORMANCE

User Epochs = 100 Epochs = 250

TPR (%) FPR (%) TPR (%) FPR (%)

1 0.00 9.66 0.00 7.88

2 12.00 12.00 8.00 1.20

3 22.00 25.00 19.45 22.16

4 1.00 58.00 2.00 28.98

5 21.00 7.87 22.00 5.78

6 28.00 14.30 17.00 0.00

7 0.00 63.00 0.00 56.78

Total 12.00 27.12 9.78 17.54

IV. Comparative Analysis

The comparative analysis of various methods is demonstrated by the graph

given below.

0 5 10 15 20 25 30

OCR 5 25 40 28 49 56 60

KSOM 10 20 50 35 58 70 85

0

10

20

30

40

50

60

70

80

90

Tru

e P

osi

tive

Ra

te

False Positive Rate

ROC Curve Between TPR Vs FPR



ISSN: 1548-7741

www.joics.org881

Figure 3: Comparative analysis of different methods

V. Conclusions

This research focuses on building an online sign verification scheme exploiting Kernel

SOMand is evaluated for objective design. The signature possesses three dynamic

characteristics which are calculatedand 5 distinct topographies are extracted from each

characteristic. Then a Kernel SOM neural network is qualified and verified for various input

training data sets. The results obtained from these data sets tend to be fascinating. Changes

need to be made for improving the TPR and FPR by expandingthe amount of reference

sample size and the no of features. A poor choice of selecting the distinct features results in

functioningdeterioration. Here,3rd central moment possessed a high degree of sensitivity and

degradedschemeoperation. In order to expand thesystemimplementation, it was rejectedlater.

Each signature differs based on environment and state from where it is gained. In terms of

capturing all the possibilities from a reference sample set, it is ideal to obtain the reference

sample set from various environments and conditions. The proposed KSOM classification

method gives higher performance in terms of accuracy value with 99% TPR, 89% FPR

compared to 66% TPR, 73% FPR in ACT and 50% TPR,56% FPR in ICDAR datasets

respectively.

References

[1]. Arica, N., Vural, F. T. Y., An Overview of Character Recognition focused on Offline

Handwriting, IEEE Transactions on Systems, Man and Cybernetics – Part C:

Applications and Reviews, 31(2), pp 216–233, 2001.

[2]. Bunke, H., Wang, P. S. P. (Editors), Handbook of Character Recognition and

Document Image Analysis, World Scientific, 1997.

10 20 30 40 50 60 70 80 90 100

OCR 0.15 0.1 0.25 0.4 0.3 0.63 0.68 0.72 0.75 0.79

KSOM 0.1 0.34 0.56 0.62 0.74 0.78 0.83 0.89 0.93 0.99

0

0.2

0.4

0.6

0.8

1

1.2

Acc

ura

cy

Epochs

Performance Between OCR Vs KSOM



ISSN: 1548-7741

www.joics.org882

[3]. Chaudhuri, A., Some Experiments on Optical Character Recognition Systems for dif-

ferent Languages using Soft Computing Techniques, Technical Report, Birla Institute

of Technology Mesra, Patna Campus, India, 2010.

[4]. Cheriet, M., Kharma, N., Liu, C. L., Suen, C. Y., Character Recognition Systems: A

Guide for Students and Practitioners, John Wiley and Sons, 2007.

[5]. Dholakia, K., A Survey on Handwritten Character Recognition Techniques for

various Indian Languages, International Journal of Computer Applications, 115(1),

pp 17–21, 2015.

[6]. Mantas, J., An Overview of Character Recognition Methodologies, Pattern

Recognition, 19(6), pp 425–430, 1986.

[7]. Rice, S. V., Nagy, G., Nartker, T. A., Optical Character Recognition: An Illustrated

Guide to the Frontier, The Springer International Series in Engineering and

Computer Science, Springer US, 1999.

[8]. Schantz, H. F., The History of OCR, Recognition Technology Users Association,

Manchester Centre, VT, 1982.

[9]. Scurmann, J., Reading Machines, Proceedings of International Joint Conference on

Pattern Recognition, Munich, pp 1031–1044, 1982.

[10]. Singh, S., Optical Character Recognition Techniques: A Survey, Journal of Emerging

Trends in Computing and Information Sciences, 6 (4), pp 545–550, 2013.

[11]. Young, T. Y., Fu, K. S., Handbook of Pattern Recognition and Image Processing,

Academic Press, 1986.

[12]. Yu, F. T. S., Jutamulia, S. (Editors), Optical Pattern Recognition, Cambridge

University Press, 1998.

[13]. Kai Ding, Zhibin Liu, LianwenJin, Xinghua Zhu, A Comparative study of GABOR

feature and gradient feature for handwritten 17hinese character recognition,

International Conference on Wavelet Analysis and Pattern Recognition, pp. 1182-

1186, Beijing, China, 2-4 Nov. 2007.

[14]. Pranob K Charles, V.Harish, M.Swathi, CH.Deepthi, "A Review on the Various

Techniques used for Optical Character Recognition", International Journal of

Engineering Research and Applications, Vol.2, Issue 1, pp. 659-662, Jan-Feb 2012.

[15]. Om Prakash Sharma, M. K. Ghose, Krishna Bikram Shah, "An Improved Zone Based

Hybrid Feature Extraction Model for Handwritten Alphabets Recognition Using

Euler Number", International Journal of Soft Computing and Engineering, Vol.2,

Issue 2.

Biographies

R. Ravi Chakravarthi, is a Computer Technology doctoral candidate in

Manonmaniam Sundaranar University, Tamilnadu, India, received his Master’s

degree in Computer Technology from Bharathiar University, Tamilnadu, India,

in 1999. His research interests include online digital signature recognition, big

data, Internet of Things, Software Testing Measurements and Project

Management.



ISSN: 1548-7741

www.joics.org883

E. Chandra, received her Ph.D. from Alagappa University, Tamilnadu,

India, in 2007. She is a Professor of Computer Science, in Bharathiar

University, Tamilnadu, India. Her research interests include online

digital signature recognition, speech recognition system, neural

networks, fuzzy logic and machine learning. Life Member in reputed

professional bodies like ACM, CSI and ACSIT.



ISSN: 1548-7741

www.joics.org884

online digital signature recognition using kernel based ...joics.org/gallery/ics-1282.pdf ·...

Documents