ing Various Techniques

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A SURVEY ON HANDWRITTEN RECOGNITION USING VARIOUS TECHNIQUES

ABSTRACT

This paper focuses a complete analysis of Handwritten Character Recognition (HCR) and its techniques.

The handwritten character recognition has been applied in variety of applications like anking sectors! Health care

industries and many such organi"ations where handwritten documents are dealt with. Handwritten Character

Recognition is the process of conversion of handwritten te#t into machine readable form. $or handwritten characters

there are difficulties like it differs from one writer to another! even when same person writes same character there is

difference in shape! si"e and position of character. %atest research in this area has used different types of method!

classifiers and features to reduce the comple#ity of recogni"ing handwritten te#t.

INTRODUCTION

Handwritten Character Recognition is a process of transforming handwritten te#t into machine e#ecutable

format. There are mainly three steps in pattern recognition& observation! pattern segmentation and pattern

classification. Recognition of character has become very interesting topic in pattern recognition for the researchers

during last few decades. 'n general! handwritten recognition is classified in to two types as online and offline

recognition methods. ffline handwriting recognition involves the automatic conversion of te#t into an image into

letter codes which are usable within computer and te#tprocessing applications. The data obtained by this form is

regarded as a static representation of handwriting. ut! in the online system! the two dimensional coordinates of

successive points are represented as a function of time and the order of strokes made by the writer are also available.

ffline character recognition is comparatively more challenging due to shape of characters! great variation of

character symbol! different handwriting style and document quality.

*everal applications including mail sorting! bank processing! document reading and postal address

recognition require offline handwriting recognition systems. +s a result! the offline handwriting recognition

continues to be an active area of research towards e#ploring the newer techniques that would improve recognition

accuracy.


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BENEFITS AND ADVANTAGES

Handwriting recognition plays an important role in the storage and retrieval of crucial

handwritten information

DATA STORAGE

,any contracts! files and personal records contain typed and handwritten information. This means that

storing such information requires physical space because those original signatures and notes cannot be electronically

stored. Handwriting recognition software allows users to translate those signatures and notes into electronic words.

-lectronic storage of this data requires far less physical space than the storage of physical copies. -lectronic storage

also requires fewer onhand employees to sort through! organi"e and upkeep the data storage warehouse.

DATA RETRIEVAL

hysical data retrieval requires personnel to sort through physical copies of old information. The data must

have been stored and correctly organi"ed as well as for the proper maintenance and upkeep on the physical copies.

/ou perform electronic data retrieval by using a file search of specific keywords! such as names or dates.

Handwriting recognition software allows for old files to be saved in a proper electronic format. ,edical records are

more available to physicians who rely on old data to diagnose illness and know a patient0s history. /ou can review

and update old contracts without risking a loss of vital information because of misfiling or physical data corruption.

KEYWORDS

Handwritten character! reprocessing! *egmentation! $eature e#traction! Classification


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ARCHITECTURE OF A GENERAL HANDWRITTEN RECOGNITION SYSTEM:

The ma1or steps involved in recognition of characters include! preprocessing! segmentation! feature e#traction andclassification

$ig 2 Stages of Ca!a"te! Re"og#$t$o#

A!"$te"t%!e of a "a!a"te! !e"og#$t$o# s&ste'

Preprocessing

Segmentation

eature E!traction

"lassi#cation

Post Processing


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(RE (ROCESSING

reprocessing can be defined as cleaning the document image and making it appropriate for input to the

CR engine. ma1or steps under preprocessing are&

3oise removal

*kew detection4correction

inari"ation

The 3oise introduced by the optical scanning devices in the input leads to poor system performance. These

imperfections must be removed prior to character Recognition. 3oise can be introduced in an image during image

acquisition and Transmission. 3oise can be of different types as 5aussian noise! 5amma noise! Rayleigh noise!

-#ponential noise! 6niform noise! *alt and pepper noise! eriodic noise etc. 3oise can be removed using 'deal

filters! utterworth filters and 5aussian filters. There is a possibility of rotation of image while scanning. *kew

detection and correction is used to align the paper document with the coordinate system of scanner. 7arious skew

detection techniques are pro1ection profiles! connected components! Hough transform! clustering etc. 'n

inari"ation! color or greyscale image is converted into binary image with the help of thresholding. inary image

can be achieved using +daptive thresholding! 5lobal thresholding! variable thresholding! stu8s method etc.

,orphological operations are also used in preprocessing. 9ilation and -rosion are the morphological operations

that increase or decrease the image si"e. -rosion makes an ob1ect smaller by eroding away the pi#els from its edges.

-very ob1ect pi#el that is touching background pi#els is changed into background i#el. However! dilation makes an

ob1ect larger by adding pi#els around its edges. -very pi#el that is touching an ob1ect pi#el is changed into ob1ect

pi#el. ther morphological operations are opening and closing.

SEGMENTATION


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*egmentation is needed since handwritten characters frequently interfere with one another. Common ways

in which characters can interfere include& overlapping! touching! connected! and intersecting pairs etc. 'n order to

separate te#t from graphs! images! line! te#t4graphics segmentation is required. The output should be an image

consisting of te#t only. Character segmentation will separate each character from another. 't is one of the main steps

especially in cursive scripts where characters are connected together. The isolated characters obtained as a result of

character segmentation are normali"ed to specific si"e for better accuracy. $eatures are e#tracted from the characters

with the same si"e in order to provide data uniformity. Christopher -. 9unn and . *. . :ang used a series of region

finding! grouping! and splitting algorithms. Region finding will identify all the dis1oint regions. The pi#els are

originally labeled n4ff where ;on< signifies the data areas. 'mage is e#amined pi#el by pi#el until ;on< value is

found. nce found it is labeled with new region number and its neighbors are searched for additional ;on< value.

*earch proceeds until no ;on< value is found. The result is that all dis1oint regions will be identified and all pi#els in

any region will be labeled with a unique number. 5rouping deals with the characters which have separate parts or

which are broken. + smallest bounding bo# is calculated that completely encloses another region. 'f for any two

regions the bounding bo# of one region completely encloses another region! then the enclosed region is relabeled to

the value of the enclosing region. Thus! the resulting region is composed of two dis1oint subregions. This is helpful

for connecting regions that have been separated due to noise .*plitting deals with touching characters. +nshul ,ehta

used Heuristic segmentation algorithm which scans the hand written words to identify the valid segmentation points

between characters. The segmentation is based on locating the arcs between letters! common in handwritten cursive

script. $or this a histogram of vertical pi#el density is e#amined which may indicate the location of possible

segmentation points in the word. ther character segmentation approaches are Thinning based method! Contour

$itting method! Robust *tatistical technique! Hypothesis 7erification! *hape $eature 7ector method etc.

FEATURE E)TRACTION

$eature e#traction is finding the set of parameters that define the shape of a character precisely and

uniquely. $eature e#traction methods are classified into three ma1or groups as.

*tatistical features.


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5lobal transformation and series e#pansion.

5eometric and topological features

*tatistical features represent the image as statistical distribution of points. 7arious methods which use

statistical features are =oning! Crossings and 9istances! ro1ections etc. 'n global transformation and series

e#pansion various techniques are $ourier transform! 5abor transform! $ourier 9escriptor! wavelets! moments!

>arhunen%oeve e#pansion etc. 'n 5eometric and topological features! the structural features like loops! curves!

lines! Tpoint! cross! opening to the right! opening to the left etc. are used. The various categories are coding

(freeman chain code)! e#tracting and counting topological structures! graphs and trees. 5eometric features are used

along with fu""y logic to recogni"e characters. +dnan +min and uttipong ,ahasukhon used structural information

to e#tract features from a character like reakpoints! 'nflection oint! Cusp oint! *traight %ine! Curve! pen or

Close %oop etc. reakpoint divides a path into sub paths. 't has two possible conditions'nflection oint (change in

curvature) and Cusp oint (sharp change in direction).*traight line has two points in sequence in a path. pen curve

is as in letter ;*


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diagonal pro1ection is computed simply by grouping pi#els by the two diagonal lines. The values of each pro1ection

are normali"ed to a range ?A2@ through the division by the ma#imum value. The normali"ed features are

concatenated in a single vector containing 2DF features. 'n ,ulti =oning an , # 3 character image is divided into

several subimages and the percentage of black pi#els in each *ubimage is used as feature. 't is a statistical

approach as features are calculated based on the number of pi#els used to represent an image. ther feature

e#traction algorithms used are Concavities ,easurement! ,+Tbased 5radient 9irectional features! 5radient

9irectional features! ,edian 5radient features! Camastra G9 features.

CLASSIFICATION

The classification is the process of identifying each character and assigning to it the correct character class.

The classification techniques can be categori"ed as&

Classical techniques.

*oft computing techniques.

The various classical techniques are Template matching! *tatistical techniques! *tructural techniques.

:hereas the various soft computing techniques are 3eural networks! $u""y logic! -volutionary computing

techniques. +dnan +min and :. H. :ilson used 3eural network for classification of characters with three layers

namely 'nput layer! utput layer and Hidden layer. The geometric features e#tracted like dot! line! curve or loops are

given as input to the input layer. -ach component of the segmented representation is classified as a dot! line! curve!

or loop. 'n each case! the characteristics of the component are determined& if a line! what are its orientation and its

si"e relative to the character frame short! medium or long. ne input neuron is used to encode each of these

possible choices (short4medium4long) and each of four possible orientations for a line. ne input neuron is used to

encode the characteristics of each component e#tracted by geometric feature e#traction technique. 3euron has two

modes of operations as training mode and testing mode.

'n the training mode! the neuron can be trained to fire (or not)! for particular input patterns. 'n the testing mode!

when a taught input pattern is detected at the input! its associated output becomes the current output. 'f the input


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pattern does not belong in the taught list of input patterns! the firing rule is used to determine whether to fire or not.

+nshul ,ehta! ,anisha *rivastava used three networks for the recognition of DI lowercase and DI upper case letters

as ,ultilayer erception(,%)! Radial asis $unction (R$) and *upport 7ector ,achine(*7,).,ultilayer

perception is a feed forward neural network with one or more layers between input and output layer. Radial basis

function (R$) networks typically have three layers& an input layer! a hidden layer with a nonlinear R$ activation

function and a linear output layer.

(OST*(ROCESSING

ostprocessing mainly consists of two tasks Joutput string generation and error detection4correction.

utput string generation will reassemble the strings which have been separated in the process of segmentation

whereas error detection4correction will correct errors with the help of dictionary.

ANALYSIS OF HANDWRITTEN ALGORITHMS

Resea!"e! Dataset (!e*(!o"ess$#g Seg'e#tat$o# Feat%!e C+ass$f$"at$o#

A""%!a"&E,t!a"t$o# - Re"og#$t$o#

Hori"ontal$eed forward

histogramback

+.5eorge profile! Contourlet propagation KL.GM

et.al.?G@ vertical transformneural networkhistogram

algorithmprofile

=oning

,edian 9ensity (=9)

and ack *7, (supportfiltration!

>.*ingh LAAA ground vectordilation! some KE.AM

et.al.?@ samples 9irectional machines)morphological

9istribution classifieroperations

(99)features

+daptivehistogram $eature

equali"ation vector of four

samples algorithm! different $eed forwardmedian filter profiles back

+. 9esai?E@ from GAA F2.IIMand nearest hori"ontal! propagation

peopleneighborhood vertical and neural network interpolation two

algorithm! skew diagonals

correction

$ourdirectional

Canny method! local feature,d.*aidur 2IAA using thinning vector by

C+ and *7, KD.EMet.al.?I@ numerals and dilation kirsch mask


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algorithm and oneglobal feature

vector

CONCLUSION

The ma1or approaches used in the field of handwritten character recognition during the last decade have

been reviewed in this paper. 9ifferent preprocessing! segmentation! feature e#traction! classification techniques are

also discussed. Though! various methods for treating the problem of hand written -nglish letters have developed in

last two decades! still a lot of research is needed so that a viable software solution can be made available. The

e#isting CR for handwritten has very low accuracy. :e need an efficient solution to solve this problem so that

overall performance can be increased.

REFERENCES

?2@ Rafael ,. . Cru"! 5eorge 9. C. Cavalcanti and Tsang 'ng Ren ;+n -nsemble Classifier $or ffline Cursive Character

Recognition 6sing ,ultiple $eature -#traction Techniques< '--- DA2A.

?D@ +nshul ,ehta! ,anisha *rivastava! Chitralekha ,ahanta ;ffline handwritten character recognition using neural

network< '--- DA22 'nternational conference on computer applications and 'ndustrial -lectronics.

?G@ ivind 9ue Trier!+nil > Nain and Torfinn Te#t ;feature e#traction methods for character recognition a survey< 2KKI

?@ Nayashree R rashad! 9r. 6 7 kulkarni ;Trends in handwriting recognition< '--- DA2A! Tthird international conference

on emerging trends in engineering and technology.

?E@ Christopher -. 9unn and . S. . :ang ;Character *egmentation techniques for handwritten te#t+ *urvey< '---

2KKD.

?I@ +dnan +min and :. H. :ilson ;Handrinted Character Recognition *ystem 6sing +rtificial 3eural 3etworks< '---

2KKG.

?L@ uttipong ,ahasukhon! Hossein ,ousavine"had! Neong/oung *ong ;Handrinted -nglish Character Recognition

based on $u""y Theory


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?F@ /uk /ing Chung! ,an to :ong handwritten character recognition by $ourier descriptors and neural network< '---

2KKL! *peech and 'mage Technologies for computing and telecommunication.

?K@ *habana ,ehfu"! 5auri katiyar ;'ntelligent *ystems for ff%ine Handwritten Character Recognition& + Review

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