5 keerthi report new

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Online Handwriting Recognition

Dept. Of CSE, JNNCE Page 1

CHAPTER-1

INTRODUCTION

Since the inception of computers we are witnessing a great deal of research activities

in the field of computer human interface. The input devices such as keyboard and

mouse have limitations in comparison with input through natural handwriting. The

natural handwriting is a very easy way of exchanging information between computers

and human beings. Also, it is difficult to input data to computers for scripts Chinese

and Japanese as these scripts have a large number of alphabets. It is also difficult to

input data for computers for Indian language scripts owing to their complex typing

nature. Two quick and natural ways of communication between users and computers

are inputting the data through handwritten documents and through speech. Speech

recognition has limitations in noisy environment and especially where privacy of an

individual is required. This work focuses on the problem of handwriting recognition

only.

1.1 Handwriting recognition

The technique by which a computer system can recognize characters and other

symbols written by hand in natural handwriting is called handwriting recognition

system [1]. Handwriting recognition has been a popular area of research since few

decades under the purview of pattern recognition and image processing. Handwriting

recognition can be broken down into two categories: off-line and on-line.

Off-line

Off-line character recognition takes a raster image from a scanner (scanned

images of the paper documents), digital camera or other digital input sources. The

image is binarized through threshold technique based on the color pattern (color or

gray scale), so that the image pixels are either 1 or 0 [2].

On-line

In on-line, the current information is presented to the system and recognition

(of character or word) is carried out at the same time. Basically, it accepts a string of

( x, y) coordinate pairs from an electronic pen touching a pressure sensitive digital

tablet.


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On-line handwriting recognition system, by contrast, captures the temporal or

dynamic information of the writing, enhances the accuracy over off-line. Another

advantage is interactivity, which means recognition errors can be corrected

immediately with the series of test. Yet, adaptation of any drawings of character is

also an advantage over off-line. When the user faces that some characters are notrecognized accurately, user can alter his way of drawing until it recognizes. It means

user can adapt to the machine. Conversely, recognizers are capable of adapting users

drawing, usually by storing possible samples from a large number of users for

subsequent recognition. Thus, there is adaptation of user to machine and of machine

to user. Electronic pen input is the direct method to compare with the both off-line

and key-board entry to the system having recognition intelligence. In addition, on-line

recognition improves the work-flow, the information is immediately available.

However, the natural and comfortable style in writing effectively reduces difficulty atthe threshold of using computers for common users. Moreover, it is recently showed

that handwriting input is the most acceptable and welcomed input style.

A handwriting recognition system can further be broken down into two

categories of writer independent and writer dependent.

Writer Independent and Writer Dependent

A writer independent recognition system recognizes wide ranges of possible writing

styles, while a writer dependent recognition system is trained to recognize only from

specific users. Therefore, a writer dependent recognition system works on data with a

smaller variability and therefore a chance of having higher reliability is achieved in

contrast to writer independent recognition system. Writing ones style brings

unevenness in writing units, which is the most difficult part. Variability in stroke

numbers, their order, shape and size, tilting angle and similarity among characters

from one another are found more often in writer independent recognition system.

Broadly, there are two kinds of writing styles. They are hand printed and

cursive handwriting. In cursive style, strokes are deliberately linked forming one from

many to draw the character, while in hand printed style possible number of strokes are

used, each stroke has significant role to complete the character. In cursive style, the

important information such as intersections, loops, curves, straight lines and hooks

etc. are missing. Sometimes, both writing styles are mixed. Natural handwriting

contains all types of styles in writing from any of the users. Specifically, the writing is

said to be natural as if users write on a piece of paper.


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With the introduction of portable hand held computers and computing devices

such as PDAs (Personal Digital Assistant), non-keyboards and non-keypads based

methods for inputting data are receiving more interest in both academic and

commercial research communities. The most promising options are pen based and

voice based inputs. Pen based method in inputting can be either off-line or on-line.

1.2 Challenges in online handwriting recognition system

A variation in handwriting is the prominent problem and achieving high degree of

accuracy is a tedious task. These variations are caused by different writing styles.

Variation in handwriting among different writers occurs since each writer possesses

own speed of writing, different styles, sizes or positions for characters or text.

Variation in handwriting styles also exists within individual persons handwriting.

This variation may take place due to: writing in various situations that may or may not

be comfortable to writer; different moods of writer; style of writing same characters

with different shapes in different situations or as a part of different words; using

different kinds of hardware for handwriting.

1.2.1 Handwriting styles variations

Handwriting styles variations depend on alignments and the different form of

characters. These variations are geometrical in nature. Common geometrical

properties are position, size, aspect ratio of strokes or characters, retraces, slant of

strokes and number of strokes in a character. Fig. 1.1 illustrates the few samples of

Gurmukhi characters from five different writers. One can note that variations exist in

each sample of a character. Fig. 1.2 illustrates five samples of few characters of

Gurmukhi script from individual writer. One can note that some kind of variations

also exists in each sample of a character although such samples share high degree of

similarities. The shape of a character is also influenced by the word in which it is

appearing. Characters can look similar although their number of strokes, and the

drawing order and direction of the strokes may vary considerably [3].

1.2.2 Constrained and unconstrained handwriting

Handwriting styles could be constrained or unconstrained [4]. Constrained

handwriting is boxed discrete and spaced discrete in nature. Unconstrained

handwriting is cursive or mixed cursive in nature. In boxed discrete handwriting, each

character is written inside a special box. Fig. 1.3 illustrates the boxed discrete


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handwriting. When each character is written separately with spaces and no character

touches other character is called spaced discrete handwriting. If each character is

written separately and touches other characters, it is referred as run-on discrete

handwriting. When characters in one word are connected and strokes are used more

than once in individual character, it is referred to cursive handwriting.It is observed that most of the people write in mixed cursive styles that

includes mixture of spaced, run-on discrete and cursive styles handwriting. Spaced

discrete, run-on discrete, cursive and mixed cursive handwriting styles are illustrated

in Fig. 1.4.

It is a difficult task to recognize cursive handwriting due to great amount of

variability. Each writer is having ones own speed of writing and uses different shapes

to represent characters. Also, in cursive handwriting no clear boundaries are specified

between characters to distinguish between them.

Fig. 1.1: Variation in characters handwritten by five writers.


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Fig. 1.2: Variation in characters handwritten by same writer.

Fig. 1.3: Boxed discrete handwriting.

Fig. 1.4: Different styles of handwriting.


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1.2.3 Personal, situational and material factors

The personal factors in handwriting variations include writers handedness. A writer

is either left handed or right handed. It has been noted that left and right handed

people use different positions and directions in handwriting. A good recognition

requires neat and clean handwriting. In most of the cases, it has been noted that neatand clean handwriting does not take place as handwriting of people also depends on

their profession [5].

The situational factors depend on the way of presentation of writing. The way

of presentation could be stressful or in haste or distraction while writing. The material

factors depend on the hardware used in writing. The material used in writing may

provide comfort or discomfort to writer that result into variations in handwriting. This

includes the position and size of writing board. The length of the writing line or the

size of the writing boxes for characters could have effect on the handwriting style.

1.3 Applications

Character and handwriting recognition has a great potential in data and word

processing for instance, automated postal address and ZIP code reading, data

acquisition in bank checks, processing of archived institutional records, etc.

Combined with a speech synthesizer, it can be used as an aid for people who are

visually handicapped. As a result of intensive research and development efforts,

systems are available for English language. However, less attention had been given to

Indian language recognition. Main reasons for this slow development could be

attributed to the complexity of the shape of Indian scripts, and also the large set of

different patterns that exist in these languages, as opposed to English.

1.4 Problem Statement

This work aims at developing a real-time on-line handwriting recognition system for

Indian languages - Hindi on handheld device such as mobile phone. The system has to

recognize all characters of Hindi alphabet and display the recognized character in

Hindi font in the device display area.


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1.5 Objectives

Objectives of the system are as below:

To develop a system that will correctly and efficiently recognizes and displaythe handwritten characters on display area of the handheld device.

System should be writer independent. System should be memory and processing efficient.

1.6 Expected Outcome

The system developed should be able to capture handwritten characters of the users

drawn in the handheld device. The characters have to be recognized within small

duration of time and displayed in Hindi font in the display area of the handheld

device.


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CHAPTER-2

SYSTEM METHODOLOGY

The established procedure to recognize online handwritten characters includes

following phases: data collection, preprocessing, feature extraction or computation of

features, segmentation, recognition and post-processing. This Chapter discusses each

phase used in a typical on-line handwriting recognition system. The output obtained

from one phase becomes input for the next phase. These phases are illustrated in

Fig. 2.1.

Fig. 2.1: Phases of handwriting recognition.

2.1 Data collection

Online handwriting recognition requires a transducer that captures the writing as it is

written. The most common of these devices is the electronic tablet or digitizer. These

Data Collection

Preprocessing

Feature Extraction

Segmentation

Recognition

Post - Processing


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devices uses a pen that is digital in nature. Data collection is the first phase in online

handwriting recognition that collects the sequence of coordinate points of the moving

pen. A typical pen includes two actions, namely, PenDown and PenUp. The

connected parts of the pen trace between PenDown and PenUp is called a stroke.

These pen traces are sampled at constant rate, therefore these pen traces are evenlydistributed in time and not in space. The common names of electronic tablet or

digitizer are personal digital assistant, cross pad and tablet PC. The appearances of

personal digital assistant, cross pad and tablet PC are shown in Fig. 2.2.

Fig. 2.2: Commonly used hardware devices for capturing handwriting.

The selection of these hardware devices is mainly based on compatibility with

operating system in use, active area dimensions and report rate of pen movements.

2.2 Preprocessing

Preprocessing phase in handwriting recognition is applied to remove noise or

distortions present in input text due to hardware and software limitations in

comparison withsmooth handwriting. These noise or distortions include irregular size

of text, missing points during pen movement collections, jitter present in text, left orright bend in handwriting and uneven distances of points from neighboring positions.

In online handwriting recognition, preprocessing includes five common steps,

namely, size normalization and centering, interpolating missing points, smoothing,

slant correction and re-sampling of points. These steps are illustrated in Fig. 2.3.


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Fig. 2.3: Common steps in preprocessing phase.

2.2.1 Size normalization and Centering

Size of the input stroke depends on how user moves the pen on writing pad. Stroke is

not generally centered when the pen is moved along the border of writing pad. Size

normalization and centering of stroke is a necessary process that should be performed

in order to recognize a character [6].

This can be achieved by comparing input stroke border frame with assumed

fixed size frame and further can be moved along with the assumed center location.

Fig. 2.4 illustrates size normalization.

Preprocessing Phase

Size normalization and

centering

Interpolating missing

points

Smoothing

Slant correction

Resampling of points


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Fig. 2.4: (a & c) Input character (b & d) size normalization and centering.

2.2.2 Interpolating missing points

The stroke drawn with high speed will have missing points. These missing points can

be calculated using various techniques such as Bezier and B - Spline [7]. In piecewise

interpolation technique, a set of consecutive four points is considered for obtaining

the Bezier curve. The next set of four points gives the next Bezier curve. Fig.2.5

illustrates this step.

Fig. 2.5: Interpolating missing points.


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2.2.3 Smoothing

Flickers exist in handwriting because of individual handwriting style and the

hardware used. These flickers can be removed by modifying each point of the list

with mean value of k-neighbors and the angle subtended at position from each end

[8].

2.2.4 Slant correction

Handwritten words are usually slanted or italicized due to the mechanism of

handwriting and the personality. The main techniques for slant estimation and

correction are run length based technique, projection method, extrema method and

generalized chain code estimator [9]-[12]. Fig.2.6 illustrates smoothing and slant

correction.

Fig. 2.6: Stroke before and after slant correction.

2.2.5 Resampling of points

Resampling of points is required to keep the points in the list at equal distances, as far

as possible. For any pair of points in the list having a distance greater than one, we

add a new point between such pairs. Any pairs having distance less than one is

untouched. The list obtained after the resampling of points is preprocessed. Fig. 2.7

shows the shape of stroke after applying the process of resampling of points [13].


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Fig. 2.7: Stroke before and after resampling of points.

2.3 Feature extraction

In the process of handwriting recognition, it is important to identify correct features.

Feature extraction is essential for efficient data representation and for further

processing [14]. Also, high recognition performance could be achieved by selecting

suitable feature extraction method. Computational complexity of a classification

problem can also be reduced if suitable features are selected.

Features vary from one script to another script and the method that gives better

result for a particular script cannot be applied for other scripts. Also, there is no

standard method for computing features of a script.

In feature extraction stage each character is represented as a feature vector,

which becomes its identity. The major goal of feature extraction is to extract a set of

features, which maximizes the recognition rate with the least amount of elements. Due

to the nature of handwriting with its high degree of variability and imprecisionobtaining these features, is a difficult task. Features are classified into two categories,

namely, low-level and high-level features also called Statistical and Structural

features respectively.


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2.3.1 Structural features

Structural features are those which provide useful information such as loops,

crossings, headline, straight line and dots. These features are derived on the basis of

calculating low-level features such as directions, positions, slope, area and slant etc. in

a stroke.

Loops

Loop recognition includes three stages: first stage finds the existence of a loop in an

alphabet, second stage determines direction (left or right), third stage finds position

(top, mid or bottom) with respect to upper-middle and middle-lower zones partitions.

The existence of a loop in an input handwritten stroke is shown in Fig. 2.4.

Overwriting in a single stroke can also result into a loop, preprocessing removes such

redundancy in data and also such loops can be avoided with the knowledge of loop

position, loop area, loop width and loop height.

Crossings

Crossings, as shown in Fig. 2.8, are the intersection of strokes. Number of crossings

in a character becomes meaningful when two or more strokes intersect. Position for

the point of intersection classifies characters into various subgroups that are further

helpful in narrowing the search operation.

Headline

Headline exists at the partition of upper-middle region and is horizontal in nature as

shown in Fig. 2.8. The identification of headline stroke narrows the search operation

as character is considered as a group of strokes. Horizontal nature of headline stroke

is computed on the basis of direction, curliness, position, slope and non-loop nature of

stroke.

Straight line

Straight line, as shown in Fig. 2.8, exists in some of the characters of Hindi script.

The straight line can be found on the basis of direction, position, crossings, headline,

curliness and non-loop nature of a stroke.

Dots

Dots are the isolated strokes as illustrated in Fig. 2.9. The dot feature can be identified

on the basis of stroke length, stroke direction, stroke position and nature of points in

strokes.


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Fig. 2.8: Positions of headline, crossing and straight line in a character.

Fig. 2.9: Characters with dot feature.

2.3.2 Statistical features

The followings are the major statistical features used for character representation:

Zoning

The frame containing the character is divided into several overlapping or non-

overlapping zones. The densities of the points or some features in different regions are

analyzed and form the representation. For example, contour direction features

measure the direction of the contour of the character, which are generated by dividing

the image array into rectangular and diagonal zones and computing histograms of

chain codes in these zones. Another example is the bending point features, which

represent high curvature points, terminal points and fork points. Fig. 2.10 indicates

contour direction and bending point features.


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Fig. 2.10: Contour direction and bending point features with zoning.

Crossings and Distances

A popular statistical feature is the number of crossing of a contour by a line segment

in a specified direction. One of the method can be, the character frame is partitioned

into a set of regions in various directions and then the black runs in each region are

coded by the powers of two. Another method can be to encode the location and

number of transitions from background to foreground pixels along vertical lines

through the word. Also, the distance of line segments from a given boundary, such as

the upper and lower portion of the frame, can be used as statistical features. These

features imply that a horizontal threshold is established above, below and through the

center of the normalized script. The number of times the script crosses a threshold

becomes the value of that feature. The obvious intent is to catch the ascending and

descending portions of the script.

Projections

Characters can be represented by projecting the pixel gray values onto lines in various

directions. This representation creates one-dimensional signal from a two dimensional

image, which can be used to represent the character image.

The next type of the statistical featuring is that the profiles. Profiles means the

distance between the bounding box of the character image and the edge of the

character. This distance is responsible for the proper reconstruction of the character in

the text format. The letters can be distinguished only if the profiles are there. Consider

that the letters p and q. Profiles are used for the detection of the contour of the

character image. The profiles distance gives the uppermost and the lowermost points

of the contour. It also helpful in maintaining the structure of the character. These are

all about the statistics of the feature extraction. The Fig. 2.11 shows projection and

profiles.


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Fig. 2.11: Projection and Profiles.

2.4 Segmentation

Segmentation is one of the phases of handwriting recognition in which data are

represented at character or stroke level so that nature of each character or stroke can

be studied individually. Segmentation is classified into two categories: external

segmentation and internal segmentation. External segmentation is performed prior to

recognition. Segmentation performed during the process of recognition is called

internal segmentation. External segmentation provides greater interactivity, savings of

computation, and simplifies the job of the recognizer. Plamondon and Srihari [15]

presented a survey on handwriting recognition systems where segmentation has been

discussed for both offline and online handwriting recognition systems.

It has been noted that segmentation study in offline handwriting recognition

system is beneficial to understand segmentation in online handwriting recognition

system as word level segmentation is one of the common task in offline and online

handwriting recognition systems. Both offline and online handwriting recognition

systems identify characters or strokes in word level segmentation.The procedure to

segment a Hindi word into characters (including core characters, and top and bottom

modifiers) is illustrated in Fig.2.12.


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Fig. 2.12: Procedure of Hindi character recognition.

The numbered arrow in Fig.2.12 represents the step of segmentation, and the

characters with solid bounding boxes are the final segmentation results. The

procedure to do character segmentation can be described as follows:

Step 1: Locate the header line and separate the core-bottom strip which contains the

core strip and bottom strip, and a top strip which contains the header line and top

modifiers.

Step 2: Identify core strip and bottom strip from the core-bottom strip, and extract

low modifiers.

Step 3: Separate core strip into characters which may contain conjunct/shadow

characters.

Step 4: Segment conjunct/shadow characters into single characters.

Step 5: Remove the header line from the top strip and extract top modifiers.

Step 6: Put header line back to the segmented core characters.


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2.5 Recognition

Statistical, syntactical and structural, neural network and elastic matching are the

commonly used handwriting recognition methods [16]. They are explained in the

following subsections.

2.5.1 Statistical methods

In statistical approach, each pattern is represented in terms of features and is viewed

as a point in dimensional space. This involves selection of features that all pattern

vectors belonging to different categories or classes to occupy disjoint region in a

dimensional space. The statistical methods are based on prior probabilities of classes

and assume variations in natural handwriting as stochastic in nature. Statistical

methods are classified as parametric and non-parametric methods. In parametric

methods, handwriting samples are statistical variables from distribution that is

characterized by a set of parameters and each class includes its own distribution

parameters. The selection of parameters is based on training data. Hidden Markov

Model (HMM) is the common example of parametric methods. Non-parametric

methods are directly estimated from training data. The nearest neighbors are the

common non-parametric methods.

Parametric methods are preferred as compared to non-parametric methods as

parametric methods are computationally easier than non-parametric methods. HMM is

the most widely used parametric statistical method applied to online handwriting

recognition systems. Initially, HMMs were applied to speech recognition. The HMMs

became popular in online handwriting recognition systems in early 1990s. HMMs

found to be suitable for cursive handwriting. The results obtained using HMMs are

reliable as outcomes are numerical values and there is always a scope to improve

recognition system using HMMs.

2.5.2 Structural and syntactical methods

Structural and syntactical methods are related to handwritten patterns where structures

and grammar are considered. Structural recognition provides a description of how the

given pattern is constructed from the primitives. This paradigm has been used in

situations where the patterns have a definite structure which can be captured in terms

of a set of rules, such as waveforms, textured images, and shape analysis of contours.

In syntactic pattern recognition, a formal analogy is drawn between the structure of

patterns and the syntax of a language. The patterns are viewed as sentences belonging


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to a language, primitives are viewed as the alphabet of the language, and the sentences

are generated according to a grammar. Thus, a large collection of complex patterns

can be described by a small number of primitives and grammatical rules. The

grammar for each pattern class must be inferred from the available training samples.

The chain codes are widely used structural representations of online handwriting.Chain code means that a stroke is temporarily divided into segments and segments are

coded. These segments are small straight lines of equal lengths and consider

information as directions, angles, geometric information in segments.

2.5.3 Neural network methods

Neural networks can be viewed as parallel computing systems consisting of an

extremely large number of simple processors with many interconnections. Neural

network models attempt to use some organizational principles such as learning,

generalization, adaptability, fault tolerance, distributed representation and

computation in a network of weighted directed graphs in which the nodes are artificial

neurons and directed edges are connections between neuron outputs and neuron inputs.

The main characteristics of neural networks are that they have the ability to learn

complex nonlinear input-output relationships, use sequential training procedures and

adapt themselves to the data. The most commonly used neural networks for pattern

classification tasks are feed-forward network, which include multilayer perceptron

and radial basis function networks. These networks are organized into layers and have

unidirectional connections between the layers. Another popular network is the self-

organizing map or kohonen network.

2.5.4 Elastic matching methods

Elastic matching is a generic operation in pattern recognition which is used to

determine the similarity between two entities. The pattern to be recognized is matched

against the stored template while taking into account all allowable changes. The

similarity measure can be optimized based on available training set. Elastic matching

is feasible but with availability of faster processors. Elastic matching is often called

deformable template, flexible matching, or nonlinear template matching [17]. Elastic

matching works very well for writer dependent data and does not require a relatively

large amount of training data [18].


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2.6 Post-processing

Post-processing refers to the procedure of correcting misclassified results by applying

linguistic knowledge. All the possible outcomes of an individual character are studied

in terms of graph and the best suitable nature of character is depicted. Post-processing

is processing of the output from shape recognition. Language information can

increase the accuracy obtained by pure shape recognition. For handwriting input,

some shape recognizers yield a single string of characters, while others yield a

number of alternatives for each character, often with a measure of confidence for each

alternative. A postprocessor can operate on this information to obtain estimates for

larger linguistic units, such as words. When the shape recognizer yields a single

choice for each character, string correction algorithms are applicable. Alternate

choices provide more information for post-processing. In post-processing, a

dictionary can be used to restrict the character combinations. This can be

implemented as a grammar that specifies all possible combinations of characters.


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CHAPTER-3

LITERATURE SURVEY

This chapter provides various schemes that are reported in the recent works for online

handwriting recognition. In the following section, we have summarized the schemes

mainly under two categories such as feature based and classifier based.

3.1 Classifier based schemes

Different classifiers such as DTW (Dynamic time warping), SVM (Support Vector

Machine), HMM, Neural network, etc., have been used for Online handwriting

recognition. The work done using these approaches is reviewed below.

3.1.1 DTW classifier

Dynamic Time Warping (DTW) classifier that can be used for handwriting

recognition is able to compare two curves in a way that makes sense to humans. As

can be seen in Fig. 3.1, DTW can compare characters in a way that is similar to the

way humans compare characters.

Fig. 3.1: Comparison of two curves using one to one comparison and DTW.

In the recognition system reported by Muralikrishna Sridhar, Dinesh

Mandalapu & Mehul Patel [19], a classifier called Active-DTW has been proposed. In

the experiments they used the database, which contains both online and off-line

handwriting information. They obtained an accuracy of 97.1% for Digits, 86.9% for

Lower case and 94.1% for Upper case English letters. Their system combines the

advantages of generative and discriminative classifiers to address the similarity of

between-class samples, while taking into account the variability of writing styles

within the same character class.

However, in order to create accurate models, a large number of training

samples is needed up front, which is not desirable or available in many practical


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applications. Vandana Roy et. al., [20] proposed a supervised adaptation framework

for the Active-DTW classifier which allows recognition to begin with a small number

of training samples, and adapts the classifier to the new samples presented to the

system during recognition. They compare the performance of Active-DTW using the

proposed adaptation framework, with a nearest-neighbor classifier, on the onlinehandwritten Tamil character dataset.

A novel framework to adapt the Active-DTW classifier has been proposed by

Vandana Roy et. al. [20]. Such an adaptation framework is particularly useful for

rapidly deploying recognizers for new scripts, since the initial requirements for

training data can be very small. A considerable improvement in accuracy of the

Active-DTW over time was shown empirically using the proposed adaptation

strategy. Also, the performance was shown to be comparable with a nearest neighbor

classifier. The memory requirements for the model data file for Active-DTW

recognizer and time taken for adaptation was shown to be significantly less as

compared to the nearest neighbor classifier.

DTW classifier reported by Muralikrishna Sridhar et. al., is for English

characters, so to enhance the recognition system for Indian script Niranjan et. al., [21]

proposed a recognition system for Tamil handwritten text. In their system they used

subspace and DTW classifiers so as to combine the advantages of the two schemes to

formulate a hybrid scheme for recognition and carried out the experiment on all the

three modes, namely, writer dependent (WD), writer independent (WI) and writer

adaptive (WA). The system proposed is not suitable for commercial use because the

objective is only to reap the advantages of both the methods.

DTW-implementation that is suitable for the automatic recognition of Tamil

handwriting is proposed by Ralph Niels and Louis Vuurpij [22] in which a prototype

based classifier that uses DTW both for generating prototypes and for calculating a

list of nearest prototypes is used. Prototypes were automatically generated and

selected. The recognition system listed above consume more space and processing

time, so to reduce both Muzaffar Bashir, and Jurgen Kempf [23] proposed a system

which uses a Dynamic Time Warping technique which reduces significantly the data

processing time and memory size of multi-dimensional time series sampled by the

biometric smart pen device BiSP. It is found that the performance of the RDTW

(Reduced DTW) method complies very well with the claims of an online recognition

system. Single characters and PIN words, handwritten by the same person can be


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recognized at an extremely high score (better 99%) with a responds time of less than

0.5 seconds. These excellent results lead to a promising application, namely the

biometric recognition of PIN codes being an essential part of the biometric two factor

person authentication method, where biometric person and PIN code recognition is

combined.

3.1.2 SVM classifier

SVM in its basic form implement two class classifications. It has been used in recent

years as an alternative to popular methods such as neural network. The advantage of

SVM, is that it takes into account both experimental data and structural behavior for

better generalization capability based on the principle of structural risk minimization

(SRM). The principle of an SVM is to map the input data onto a higher dimensional

feature space nonlinearly related to the input space and determine a separating hyper

plane with maximum margin between the two classes in the feature space.

The recognition system reported by Abdul Rahim Ahmad et. al., [24] uses

SVM for online handwriting recognition for English characters. In Online

Handwritten Character Recognition system for Devanagari and Telugu Characters,

proposed by H. Swethalakshmi et. al., [25] Support vector machines have been used

for constructing the stroke recognition engine. The main disadvantage of their

approach is that the set of baseline strokes that loops have been found to give the

lowest recognition accuracy. Swethalakshmi et. al., explored three approaches for

stroke recognition.

Single Recognition Engine approach in which each stroke is represented as an

n-dimensional feature vector depending on the choice of the number of points for

stroke representation. The features chosen to represent the curve are the co-ordinates

of points in the preprocessed stroke. Multiple SVM Engines approach in which

strokes are pre-classified into two categories based on a threshold set for curve length.

Further an SVM-based engine is constructed for each stroke category. Strokes with

curve length below the threshold are classified as small strokes. Small strokes are

subjected to normalization and smoothing Third approach is Stroke Recognition using

HMMs in which strokes are represented as variable-length sequences of frames. Each

frame consists of a feature vector representing the features captured at the

corresponding time instant. Here, they have used the co- ordinates of the point as

features for a frame.


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Recognition systems listed above do not recognize mathematical symbols; to

support this Birendra Keshari and Stephen M. Watt [26] proposed Online

Mathematical Symbol Recognition using SVMs with Features from Functional

Approximation. The experimental results show that the SVM trained using features

from functional approximation produces results comparable to the other SVM basedrecognition system. This makes the functional approximation technique interesting

and competitive since the features have certain computational advantages.

3.1.3 HMM classifier

A hidden Markov model (HMM) is a statistical Markov model in which the system

being modeled is assumed to be a Markov process with unobserved (hidden) states.

An HMM can be considered as the simplest dynamic Bayesian network. In a regular

Markov model, the state is directly visible to the observer, and therefore the state

transition probabilities are the only parameters. In a hidden Markov model, the state is

not directly visible, but output, dependent on the state, is visible. Each state has a

probability distribution over the possible output tokens. Therefore the sequence of

tokens generated by an HMM gives some information about the sequence of states. A

hidden Markov model can be considered a generalization of a mixture model where

the hidden variables (or latent variables), which control the mixture component to be

selected for each observation, are related through a Markov process rather than

independent of each other.

Hidden Markov Models (HMM) have long been a popular choice for Western

cursive handwriting recognition following their success in speech recognition. Even

for the recognition of Oriental scripts such as Chinese, Japanese and Korean, Hidden

Markov Models are increasingly being used to model substrokes of characters.

However, when it comes to Indic script recognition, the published work employing

HMMs is limited, and generally focused on isolated character recognition. In this

effort, a data-driven HMM-based online handwritten word recognition system for

Tamil, an Indic script, is proposed by Bharath A and Sriganesh Madhvanath [27]. The

accuracies obtained ranged from 98% to 92.2% with different lexicon sizes. These

initial results are promising and warrant further research in this direction. The results

are also encouraging to explore possibilities for adopting the approach to other Indic

scripts as well. So to support another popular Indian language namely Telugu

Jagadeesh Babu et. al., [28] proposed a recognition system which is based on HMM

and uses a combination of time-domain and frequency-domain features. The system


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gives top-1 accuracy of 91.6% and top-5 accuracy of 98.7% on a dataset containing

29,158 train samples and 9,235 test samples.

Parui et. al. [29] uses HMM to support Online Handwritten Bangla Character.

They used a database of 24,500 online handwritten isolated character samples written

by 70 persons. Samples in this database are composed of one or more strokes and wehave collected all the strokes obtained from the training samples of the 50 character

classes. These strokes are manually grouped into 54 classes based on the shape

similarity of the graphemes that constitute the ideal character shapes. Strokes are

recognized by using hidden Markov models (HMM). One HMM is constructed for

each stroke class. A second stage of classification is used for recognition of characters

using stroke classification results along with 50 lookup-tables (for 50 character

classes).

3.1.4 Neural Network classifier

Neural Nets (NN) and Hidden Markov Models (HMM) are the popular, amongst the

techniques which have been investigated for handwriting recognition. It has been

observed that NNs in general obtained best results than HMMs, when a similar feature

set is applied. The most widely studied and used neural network is the Multi-Layer

Perceptron (MLP). Such an architecture trained with back-propagation is among the

most popular and versatile forms of neural network classifiers and is also among the

most frequently used traditional classifiers for handwriting recognition.

Other architectures include Convolutional Network (CN), Self-Organized

Maps (SOM), Radial Basis Function (RBF), Space Displacement Neural Network

(SDNN), Time Delay Neural Network (TDNN), Quantum Neural Network (QNN),

and Hopfield Neural Network (HNN). Few attempts have been found in the literature

in which counter-propagation (CPN) architecture has been used for the recognition of

handwritten characters. Ahmed et. al., [30] made an attempt but only for digit

recognition.

Muhammad Faisal Zafar et. al., [31] proposed On-line Handwritten Character

Recognition system for upper case English alphabets which is an implementation of

Counter propagation Neural Net. CPN is more economical than convergence of other

NN architectures e.g. back-propagation where the training time can take long time.

The experiments provided the authors an opportunity to explore this pattern

recognition methodology; the exercise provided a theoretical base for further

investigations and impetus for development work in this discipline. The obtained


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results motivate the continuity of the system development considering a preprocessing

mechanism including normalization and slant removal.

Back propagation (BP) for training multilayer neural networks has many

shortcomings. Learning often takes insupportable time to converge, and it may fall

into local minima at all. One of the possible remedies to escape from local minima isusing a very small learning rate, but this will slow the learning process. Walid A.

Salameh and Mohammed A. Otair [32] proposed a system for the training of

multilayer neural networks with very small learning rate, especially when using large

training set size. It can apply in a generic manner for any network size that uses a

back propagation algorithm through optical time. They studied the performance of the

Optical Back propagation algorithm (OBP) on training a neural network for online

handwritten character recognition in comparison with back propagation.

3.1.5 Nearest-neighbor classifier

Among the various methods of supervised statistical pattern recognition, the Nearest

Neighbor rule achieves consistently high performance, without a priori assumptions

about the distributions from which the training examples are drawn. It involves a

training set of both positive and negative cases. A new sample is classified by

calculating the distance to the nearest training case; the sign of that point then

determines the classification of the sample. The k-NN classifier extends this idea by

taking the k nearest points and assigning the sign of the majority. It is common to

select k small and odd to break ties (typically 1, 3 or 5). Larger k values help reduce

the effects of noisy points within the training data set, and the choice of k is often

performed through cross-validation.

Abrita Chakravarty and William Day [33] proposed a recognition system for

handwritten digits. Nearest-neighbor (NN) and k-nearest neighbors (kNN) based

recognizers have widely been used for handwritten character recognition. When used

in applications, it is very important to compute reliable confidences corresponding to

the recognition results. The confidence values are typically computed during the post-

processing phase of the recognizer. They are the measures of correctness of output of

a recognizer. The estimation of confidences requires higher values to be assigned to

the correct recognition results, and lower values to the incorrect recognition results.

Vandana Roy and Sriganesh Madhvanath [34] have proposed A Skew-tolerant

Strategy and Confidence Measure for k-NN Classification of Online Handwritten

Characters. They explored the Adaptive-kNN strategy and confidence measure to


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address the problem of online handwritten character recognition problem in presence

of skewed training sets. They showed through experiments, that performance of the

traditional kNN recognition strategy and confidence measure is highly sensitive to the

value of k, while adaptive-kNN strategy and confidence measure is not so sensitive to

the value of k. Hence, adaptive-kNN works well on the skewed training sets. Theyalso compared the performance of Adaptive-kNN strategy and related confidence

measure with various nearest neighbor based strategies and confidences, namely NN,

kNN, ACT, and observed that Adaptive-kNN strategy and confidence measure

outperforms the NN, kNN, and ACT, when the distribution of samples across classes

is skewed. This is a very promising technique for use in applications where the

distribution of training samples is skewed due to unbalanced data collection or due to

samples getting added over a period of use.

The k-nearest neighbor (kNN) rule is a simple and effective method for multi-way classification that is much used in Computer Vision. However, its performance

depends heavily on the distance metric being employed. Large margin nearest

neighbor (LMNN) classifier learns a distance metric for kNN classification and

thereby improves its accuracy. M. Pawan Kumar et. al., [35] extend the LMNN

framework to incorporate knowledge about invariance of the data. The main

contributions of their work are three fold: (i) Invariances to multivariate polynomial

transformations are incorporated without explicitly adding more training data during

learning - these can approximate common transformations such as rotations and

affinities; (ii) the incorporation of different regularizes on the parameters being learnt;

and (iii) for all these variations, they show that the distance metric can still be

obtained by solving a convex SDP problem. They call the resulting formulation

invariant LMNN (ILMNN) classifier.

3.2 Feature based schemes

Different features of handwritten scripts such as Structural, Syntactic, etc have been

used for recognition.

3.2.1 Transformation feature

In the online character recognition system for the handwritten Kannada characters,

proposed by Srinivasa Rao Kunte R and Sudhaker Samuel R D [36], wavelet features

are extracted from the contour of characters are used as features. The conventional

feed forward multilayer neural network is used as classifier. The results obtained are


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most encouraging and the system can be extended to other similar Indian languages,

particularly, Telugu.

Rajput and Anita H. B [37] proposed a recognition system in which

recognition is based upon features extracted using Discrete Cosine Transform (DCT)

and Wavelets. The proposed method is experimented on handwritten documents ofeight Indian scripts that include English script and yielded encouraging results. The

above two listed system do not recognize numerals, to support this Diego et. al., [38]

proposed a recognition system which use Directional Continuous Wavelet Transform.

The percentage of correctly classified patterns was 99.17% and 90.20% for the

training set and the test set, respectively.

3.2.2 Structural feature

Online systems may also use features such as velocity, pressure, etc., that are captured

during writing. The temporal relations in online data are typically captured by

mathematical models like HMMs, Linear Prediction, etc., at the stroke or the sub

stroke level. Popular online handwriting recognition approaches give equal

importance to all parts of a stroke during matching, which may not be the best for all

cases. Karteek Alahari et. al., [39] proposed a recognition system which detect the

parts of a stroke (called sub stroke) that are more useful for the classification task.

Objective is to identify these sub strokes and use this information for improving the

performance of recognition schemes. Consider the problem of recognizing the

numerals 2 and 3 shown in Fig. 3.2.

Fig. 3.2: The sub strokes (sequence circled with dotted lines) of the numerals.

The two numerals appear to possess similar curvature properties at the

beginning of the sequences. As the complete numbers begin to appear, their


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distinguishing characteristics unfold over time. In other words, the tail portion of the

numbers is more useful for distinguishing them. Authors described an approach to

identify critical segments of the strokes. The individual parts are then weighed to

obtain appropriate score for the final recognition.

The dataset consists of more than 1200 online numeral and character strokescollected from different people using an IBM CrossPad. To demonstrate the

applicability of their approach for discriminating two classes, they chose similar

character/ numeral pairs. To account for the variability in the data due to translation,

they normalize the features using a bounding box for the stroke and rescaling it to the

0-1 range. The accuracy of the classifiers reported here are much lower than the

commercial or most of the reported recognition systems. This is due to the fact that (a)

datasets were not tuned to achieve higher recognition or preprocessed to suit a

specific recognition scheme, and (b) implementation of HMMs, DTW, etc. is nottuned for the online data case.

Aparna et. al., [40] proposed a system for online recognition of handwritten

Tamil characters in which handwritten character is constructed by executing a

sequence of strokes. A structure or shape-based representation of a stroke is used in

which a stroke is represented as a string of shape features. Using this string

representation, an unknown stroke is identified by comparing it with a database of

strokes using a flexible string matching procedure.

3.2.3 Statistical feature

The recognition system reported by R. J. Ramteke [41] makes use of Invariant

Moments feature for handwritten Devanagari vowels recognition. The system is

independent of size, slant, orientation, translation and other variations in handwritten

vowels. In order to enhance the performance of the system, an attempt has been made

to compute invariant moments by small perturbation in image and information is

extracted from the perturbation. But it was found that, another local feature descriptor,

image partition in different zoning is better representation of the features than

perturbation. The Fuzzy Gaussian Membership function has been adopted for

classification. The success rate of the method is found to be 94.56%.

Hiroto Mitoma et. al., [42]proposed online character recognition system based

on Elastic Matching and Quadratic Discrimination to overcome the over fitting

problem which often degrades the performance of elastic matching based online

character recognizers. In the proposed technique, elastic matching is used just as an


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extractor of a feature vector representing the difference between input and reference

patterns. They have obtained 97.95% accuracy.

3.2.4 Local features

Prasanth et. al., [43] proposed a character based elastic matching using local features

for recognizing online handwritten data. Dynamic Time Warping (DTW) has beenused with four different feature sets: x-y features, Shape Context (SC) and Tangent

Angle (TA) features, Generalized Shape Context feature (GSC) and the fourth set

containing x-y, normalized first and second derivatives and curvature features.

Nearest neighborhood classifier with DTW distance was used as the classifier. In

comparison, the SC and TA feature set was found to be the slowest and the fourth set

was best among all in the recognition rate. The results have been compiled for the

online handwritten Tamil and Telugu data. On Telugu data they obtained an accuracy

of 90.6% with a speed of 0.166 symbols/sec. To increase the speed they haveproposed a 2-stage recognition scheme using which they obtained accuracy of 89.77%

but with a speed of 3.977 symbols/sec.

3.2.5 Star feature

The features extracted from the character should encode the local, global and the

structural characteristics of the character shape. Dinesh M & Murali Krishna Sridhar

[44] proposed A Feature based on Encoding the Relative Position of a Point in the

Character for Online Handwritten Character Recognition. They proposed a new

feature for recognition of online handwritten characters called the star feature. The

star feature encodes the local, global and structural characteristics of a character. The

star feature describes every point of the character, in terms of its relative position with

respect to the other points in the character. The experimental results show that the star

feature achieves high accuracy on both the data sets.


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CHAPTER-4

SUMMARY

It is found from the literature that recognition of characters from Indian languages in

general is more difficult than for European languages because of the large number of

vowels, consonants, and conjuncts (combination of vowels and consonants).

Following are the findings obtained from the literature survey on different

classification schemes for on-line handwriting recognition.

DTW classifier requires an adaptation framework which is particularly useful

for rapidly deploying recognizers for new scripts. The memory requirements for the

Active-DTW recognizer and time taken for adaptation were shown to be significantly

less as compared to the nearest neighbor classifier. The combination of subspace and

DTW classifiers, even though compatible all the three modes, namely, writer

dependent, writer independent and writer adaptive, is not suitable for commercial use

because that is only to reap the advantages of both the methods. Prototype based

DTW classifier is more suitable for automatic recognition systems.

SVM classifier used for online handwriting recognition of Indian script has

been found to give the lowest recognition accuracy for set of baseline strokes that

loops. SVM with features from Functional Approximation results in good accuracy

for mathematical symbols. A data-driven HMM-based online handwritten word

recognition system obtained accuracies ranged from 98% to 92.2% with different

lexicon sizes. The relatively low performance in the case of high lexicon size can be

improved by the use of statistical language models, which are commonly applied in

Western cursive recognition.

Counter propagation Neural Net used as classifiers is found to be more

economical than other NN architectures such as back-propagation where the training

time can take long time but multilayer neural networks with very small learning rate

can be developed using Optical Back propagation.

In case of Nearest-neighbor classifier, performance of the traditional kNN

recognition strategy and confidence measure is highly sensitive to the value of k,

while adaptive-kNN strategy and confidence measure is not so sensitive to the value

of k. Hence, adaptive-kNN works well on the skewed training sets. However, its

performance depends heavily on the distance metric being employed, so Large margin


33/38



nearest neighbor (LMNN) classifier can improve the accuracy of recognition system

by learning a distance metric for kNN classification.

Wavelet feature based recognition system developed for Kannada characters

can be extended to other similar Indian languages, particularly, Telugu. Discriminant

sub strokes based system improves the performance of feature based recognitionschemes. Star feature which is based on encoding the relative position of a point in the

character has been found to encode the local, global and the structural characteristics

of the character shape there by providing high accuracy.


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