HANDWRITING RECOGNITION IN INTELLIGENT DESIGN...

A R C H I V E S O F M E C H A N I C A L T E C H N O L O G Y A N D A U T O M A T I O N

Vol. 34 no. 3 2014

WOJCIECH KACALAK*, MACIEJ MAJEWSKI**

HANDWRITING RECOGNITION

IN INTELLIGENT DESIGN SYSTEMS

This article presents selected research on the development of complex fundamentals of build-

ing intelligent interactive systems for design of machine elements and assemblies on the basis of

its features described in a natural language. We propose a new method for handwriting recognition

that utilizes geometric features of letters. The article deals with recognition of isolated handwritten

characters using neural networks. As a result of the geometrical analysis, graphical representations

of recognized characters are obtained in the form of pattern descriptions of isolated characters.

Selected parameters of the characters are inputs to the neural network for writing recognition

which is font independent. In this article, we present a new method for off-line natural writing

recognition and also describe our research and conclusions on the experiments.

Key words: human-machine interaction, intelligent automated design, intelligent system,

handwriting recognition, artificial neural networks, computer aided design

1. INTRODUCTION

The presented research [6] involves the development of complex fundamentals

of building intelligent interactive systems for design of machine elements and

assemblies on the basis of its features described in a natural language.

In machine automation technology, the problem of writing recognition is quite

complex [3], and even now there is no single approach that solves it both efficient-

ly and completely in all contexts. In written language recognition processes, an

image containing text must be appropriately supplied and preprocessed. Then the

text must either undergo segmentation or feature extraction. Small processed piec-

es of the text will be the result, and these must undergo recognition by the system.

Finally, contextual information should be applied to the recognized symbols to

verify the result. Artificial neural networks, applied in handwriting recognition,

allow for high generalization ability and do not require deep background

* Prof. dr hab. inż. ** Dr hab. inż.

Department of Mechanical Engineering, Koszalin University of Techno-

logy.

W. Kacalak, M. Majewski 4

knowledge and formalization to be able to solve the written language recognition

problem. Handwriting recognition can be divided by its input method into two

categories: off-line handwriting recognition and on-line handwriting recognition.

For off-line recognition, the writing is usually captured optically by a scanner. For

on-line recognition, a digitizer samples the handwriting to time-sequenced pixels

as it is being written. Hence, the on-line handwriting signal contains additional

time information which is not present in the off-line signal.

PREPROCESSING SUBSYSTEM

Module of searching of

centre of mass

Module of pattern

description

NEURAL NETWORKSUBSYSTEM

for isolated characters

Module for recognizing

isolated characters using artificial

neural networks

Natural writing

recognised text

Natural written

text

Digitization

Binarization

Noise elimination

Thinning

Normalizing

Segmentation

Module of input value

normalization

Module of fuzzy logic

rules

GEOMETRICAL ANALYSIS SUBSYSTEM

for feature extraction by character

Module of detection and extraction of geometrical

features

Module for recognizing words

using artificial neural networks

ANN trainingpatterns of words

(vocabulary verification)

Module for recognizing phrases

using artificial neural networks

ANN training patterns of phrases

(contextual knowledge from linguistics)

FUZZY LOGIC SUBSYSTEM

Letter string

recognition module

NEURAL NETWORK

SUBSYSTEM

for vocabulary and linguistics

Figure 1. Proposed handwriting recognition system [6]

In the proposed new method [7] of natural writing recognition in Figure 1, the

handwritten text is subject to the following preprocessing: digitization, binariza-

tion, noise elimination, thinning, normalizing and segmentation. The next step is

to find the center of mass of the character image. With the center of mass as

a reference point, radiuses are drawn, creating a set of points describing the con-

tour of the character so that its pattern description is made. In the proposed hybrid

system, the pattern description of each isolated character, after the process of input

value normalization and application of letter description rules using fuzzy logic,

are the input signals for probabilistic neural networks for isolated character recog-

nition. The recognized characters are grouped into more quantitative units with the

letter string recognition module, which are coded as binary images of vectors and

then become inputs of the module for recognizing words. The module uses

a 3-layer Hamming neural network [11, 12]. The network of this module uses

a training file containing patterns of words. The recognized vocabulary words

represented by the output neurons are processed by the module for recognizing

phrases which uses the Hamming Maxnet network equipped with a training file

containing phrases built with contextual knowledge from linguistics.

Handwriting recognition in intelligent design systems 5

2. THE STATE OF THE ART

The state of the art of automatic recognition of handwriting at the beginning of

the new millennium is that as a field it is no longer an esoteric topic on the fringes

of information technology, but a mature discipline that has found many commer-

cial uses. On-line systems for handwriting recognition are available in hand-held

computers such as personal digital assistants. Their performance is acceptable for

processing handprinted symbols, and when combined with keyboard entry, a pow-

erful method for data entry has been created.

Off-line systems are less accurate than on-line systems. However, they are now

good enough that they have a significant economic impact on specialized domains

such as interpreting handwritten postal addresses on envelopes and reading courte-

sy amounts on bank checks [1, 2, 3, 9, 10, 13, 19].

The success of on-line systems makes it attractive to consider developing off-

-line systems that first estimate the trajectory of the writing from off-line data and

then use on-line recognition algorithms [15]. However, the difficulty of recreating

the temporal data has led to few such feature extraction systems so far [1].

Research on automated written language recognition dates back several dec-

ades. Today, cleanly machine-printed text documents with simple layouts can be

recognized reliably by OCR software. There is also some success with handwrit-

ing recognition, particularly for isolated handprinted characters and words. For

example, in the on-line case, the recently introduced personal digital assistants

have practical value. Similarly, some online signature verification systems have

been marketed over the last few years and instructional tools to help children learn

to write are beginning to emerge. Most of the off-line successes have come in

constrained domains, such as postal addresses, bank checks, and census forms.

The analysis of documents with complex layouts, recognition of degraded printed

text, and the recognition of running handwriting continue to remain largely in the

research arena. Some of the major research challenges in on-line or off-line pro-

cessing of handwriting are in word and line separation, segmentation of words into

characters, recognition of words when lexicons are large, and the use of language

models in aiding preprocessing and recognition. In most applications, machine

performance is far from being acceptable, although potential users often forget that

human subjects generally make reading mistakes [2, 3, 5].

The design of human-computer interfaces [6, 7, 8, 12] based on handwriting is

part of a tremendous research effort together with speech recognition, language

processing and translation to facilitate communication of people with computers.

From this perspective, any successes or failures in these fields will have an im-

portant impact on the evolution of languages [4, 14].


3. DESCRIPTION OF THE METHOD

The proposed system [6] attempts to combine two methods for natural writing

recognition, neural networks and preprocessing for geometric features extraction.

The system consists of the preprocessing subsystem, geometrical analysis subsys-

tem, fuzzy logic subsystem, neural network subsystem for isolated characters as

well as neural network subsystem for vocabulary and linguistics, as shown in Fig-

ure 2. The motivation behind that preprocessor is to reduce the dimensionality of

the neural network input. However, another benefit given by the preprocessor is

immunity against image translation, because all the information is relative to the

image's center of mass.

The developed geometrical analysis is based on the processing of the images of

letter shapes into their graphical representations in the form of pattern descriptions.

The process of the geometrical analysis begins with determining of the center of

mass of a letter with a gravity method in order to find the initial point of the analy-

sis. The next step of the algorithm is based on drawing radiuses from the initial

point, the lengths of which are equal to the length of the line segment created by

the initial point and the point on the letter furthest from this point. The creation of

a circle of that radius makes it visible that the analysis covers the whole letter. The

precision of this geometrical analysis method is proportional to the number of

radiuses.

Where the radiuses intersect with the letter, points are obtained, which makes it

possible to obtain the measures of the line segment created by the initial point and

the letter intersection point. The lengths of the created line segments obtained are

represented in the form of pattern descriptions of isolated characters which are

inputs of the probabilistic neural network. Geometrical analyses of characters for

exemplary letters are shown in Figure 3, which are also used by probabilistic neu-

ral networks to recognize isolated characters.

The architecture of modified probabilistic neural networks for recognition of

pattern descriptions of isolated characters is shown in Figure 4. It is composed of

interconnected neurons organized in successive layers. The probabilistic neural

network was first introduced by Specht [16, 17, 18]. The probabilistic network

consists of input, pattern, summation and output layer.

In the proposed handwriting recognition system, because of the binary input

signals, the Hamming neural network is chosen for both the word recognition and

phrase recognition [11, 12] as shown in Figure 2. The network directly realizes the

one-nearest-neighbor classification rule.


PR

EP

RO

CE

SS

ING

SU

BS

YS

TE

M

natural written text

Digitization, Binarization,Noise elimination, ThinningNormalizing, Segmentation

M for ing ofe of m

searchodulecent r ass of

an isolated character

M for of g seometrical featureodule detection and extraction

Phrase recognition moduleusing neural networks

Word recognition moduleusing neural networks

NE

UR

AL N

ET

WO

RK

SU

BS

YS

TE

M fo

r vo

cab

ula

ry a

nd

lin

gu

istics

GEOMETRICAL ANALYSIS

SUBSYSTEM

Network input:N=a*b

Classification to phrase classes linguistics) (Contextual knowledge from

Network input:N=26*a

OUTPUT:binary images ofthe recognised

words

Cla

ssific

ation

to

word

cla

sse

s(V

ocab

ula

ry p

atte

rns)

NETWORK INPUT:binary images

as isolatedcomponents of

the textcreated by

the letter stringrecognition

module

binary image ofrecognised

word

NEURAL NETWORK SUBSYSTEM for isolated characters

Natural writing recognised text

Isolated character recognition moduleusing artificial neural networks

Letter string recognition module

Ob

raz b

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rny fra

zy

Ob

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ozpozna

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azy

WYRAZ 1WYRAZ 2WYRAZ 3

WYRAZ b

Numer wyrazu

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a

WYRAZ 1WYRAZ 2WYRAZ 3

WYRAZ b

b

RO

ZP

OZ

NA

WA

NIE

r1 r2

r3

r4

r5r6r7

r8

r9

r10

r11

r12

f=4

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Lfi

r

xc

yc

r1r2

r3

r4

r5

r6r7

r8

r9

r10

r11

r12

f=4

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Lfi

r

xc

yc

r1

r2

r3

r4

r5

r6r7

r8

r9

r10

r11

rn

f=1

f=7

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Lfi

r

xc

yc

r1 r2

r3

r4

r5r6r7

r8

r9

r10

r11

r12

f=4

Lo

Lfi

r

xc

yc

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s

o1p1

p3

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pn

s(x , y )c c

r1

r2

r3

r4

r5

r6

r7

r8

r9

r10

r11

rn

f=1f=12

f=7

Lo Lfi r

xc

yc

o2

s

o1

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p2

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p11

0

102030

405060

708090

100

1 2 3 4 5 6 7 8 9 10 11 120

102030405060708090

100

1 2 3 4 5 6 7 8 9 10 11 12

H(?r ), H(? ), H(? )fi i bi

Geometrical analysis results:

Input layer

Statepatternlayer

Summation layer

Pi

P1

(Cluster decision)

Stateclass

1

Stateclass

2

Stateclass

i

Stateclass

K

1

1

i

m

f

f

f

f

f

f

class ki

i=1, ..., K

f

(x)

(x)

(x)

(x)

(x)

(x)

2

1f

i

1f

f

f

K

Nf{ 1 2 K X , X , ..., X }

Parameter input:

Weights:

x1

x2

xj

xp

0

1

0

1

( )å=

-=

=-=

=

n

1j

2ijj

i

i

wx

wx

w,xd

å=

=n

1i

ijij xwy

å=

=n

1i

ii22 xwy

å=

=n

1i

ii11 xwyK,,2,1i KÎ

å=

=n

1i

ipip xwy

( )

( ) ( )

( ) ( )[ ]twtx

tηtw

1tw

j

j

j

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=+

å=

=n

1i

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å=

=K

1i

iNN

Pi (x

)

wp3

wpn

wp1

P i(x)

P i(x)

Pi (x)

PK (x)

Pi (x)

PK

P2

Figure 2. Methodology of the proposed system for handwriting recognition [6]


f=1f=2

f=3f=4

r1

r2

r3

r4

r5

r6r7

r8

r9

r10

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r12

f=5

f=6f=7

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f=11

f=12

r1

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f=3

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r1r12

f=12

r10

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r6r7

r8

r9

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r12

f=2

f=3

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rfn

r1

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rfx

r1

r2

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r4

r5

r6

r7

r8

r9

r10

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fnf2f1f llll +++= K

f=1

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r1

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r5

r6r7

r8

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r10

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r12

lf1

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r1

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r3

r4

r5

r6r7

r8

r9

r10

r11

r12

f=2

f=3

f=4fnf1 rr ¸ rf1

rfn

Δr

r1

r2

r3

r4

r5

r6

r7

r8

r9

r10

r11

r12

f1ff rrΔr -= +

f=1f=12

Δr

f=7

Figure 3. Geometrical analyses of characters: A) determination of the center of the mass for exem-

plary letter s; B) determination of intersection points of the letter and the radiuses for exemplary

letter o; C) measurement of the length of line segments l created by letter points in fragments f for

letter e; D) summation of measurements in fragments f containing n radiuses for letter l; E) meas-

urement of the length of line segments of each radius for exemplary letter e and letter l (F); G)

measurements of differences of the radius lengths in each fragment f for exemplary letter e and

letter l (H)


Input layerState

pattern layer

Summation layer

1

Pi

P1

P2

PK

(Clusterdecision,

competitive)output layer

Letterclass

1

Letterclass

2

Letterclass

i

Letterclass

K

1

1

1

2

1

N

2

2

2

2

N

i

i

m

i

N

f

f

f

f

f

f

class ki

i=1, ..., K

f

X x x xi 1 2 p=[ , , ..., ]Ti i i

1

1(x

)

1

2(x)

1

N (x)1

2

1(x

)

2

2(x)

2

N (x)2

i

1(x

)

i

m(x)

iN (x)i

K

1(x

)

K

2(x)

K

N (x)KP

i (x)x1

2

1f

x2

i

1f

xj

K

1f

K

2f

K

K

Nf

xp

{ 1 2 K X , X , ..., X }

wj j = xi i

Parameter input:

Weights:

x1

0

1

Normalization functionfor each parameter value

Input signal vectorsanalyzed data of

describing geometrical features

of characters

Neural network inputs: vectors with encoded character feature values

( )å=

-=

=-=

=

n

1j

2ijj

i

i

wx

wx

w,xd

å=

=n

1i

ijij xwy

å=

=n

1i

ii22 xwy

å=

=n

1i

ii11 xwyK,,2,1i KÎ

wp3

wpn

wp1

å=

=n

1i

ipip xwy

y1

yj

yp

y2

y3

P i(x)

P i(x)

Pi (x)

PK (x)

Pi (x)

( ) ( )

( ) ( )[ ]twtx

tηtw

1tw

j

j

j

-´

´+=

=+

å=

=n

1i

i3i3 xwy

å=

=K

1i

iNN

r1

r2

r3

r4

r5

r6r7

r8

r9

r10

r11

rn

Lo

Lfi

r

xc

yc

s

P1

P3

P7

Pn

s(x , y )c c

Cm

C

Z

rfi rfi rfi

r1

r2

r3

r4

r5

r6

r7

r8

r9

r10

r11

rn

Lo

Lfi

ss(x , y )c c

P2

P8

P11

Cm

C

yc

xc

Z

rm

rfirfi

rfi

0

1

0

1

Figure 4. The architecture of the probabilistic neural network for recognition of pattern descriptions

of isolated characters

4. EXPERIMENTAL RESULTS

The research on the developed method concerns the ability of the neural net-

work to learn to recognize specific letters. The neural networks are trained with

the model of isolated written language characters. Several geometrical analyses of

isolated characters and their pattern descriptions were realized (Figure 5), which

made it possible to draw significant conclusions and apply them in the proposed

algorithms. The ability of the neural network to learn to recognize specific charac-

ters depends on the type of parameters of geometrical features. The specified type

of parameters enables the network to minimize the error so that it can work more

efficiently. Based on the research, the following parameters are the most signifi-

cant for the recognition, as shown in Figure 6.


0

20

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rad

ius

len

gth

[%]

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len

gth

[%]

0

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1 2 3 4 5 6 7 8 9 10 11 12r

rad

ius

len

gth

[%]

Figure 5. Geometrical analysis and pattern description of isolated characters

r1r2

r3

r4

r5

r6r7

r8

r9

r10

r11

r12

f=4

Lo

Lfi

r

xc

yc

r1

r2

r3

r4

r5

r6

r7

r8

r9

r10

r11

rn

f=1f=12

f=7

Lo Lfi r

xc

yc

r1

r2

r3

r4

r5

r6r7

r8

r9

r10

r11

rn

f=1

f=7

Lo

Lfi

r

xc

yc

r1 r2

r3

r4

r5r6r7

r8

r9

r10

r11

r12

f=4

Lo

Lfi

r

xc

yc

r1 r2

r3

r4

r5r6r7

r8

r9

r10

r11

r12

f=4

Lo

Lfi

r

xc

yc

Δro2

s

o1p1

p3

p7

pn

s(x , y )c c

o2

s

o1

s(x , y )c c

p2

p8

p11

Figure 6. Geometrical analyses of characters for recognition of handwriting

using neural networks


5. CONCLUSIONS AND PERSPECTIVES

The presented research involves the development of complex fundamentals of

building intelligent interactive systems for design of machine elements and assem-

blies on the basis of its features described in a natural language. The advantages of

this new method of natural writing recognition are flexibility with regards to writ-

ing style, geometrical analysis enabling font independent character recognition,

possibility of application of other types of neural networks, extension of the range

of geometrical analysis and other possibilities for further development.

ACKNOWLEDGEMENT

This project was financed from the funds of the National Science Centre (Po-

land) allocated on the basis of the decision number DEC-2012/05/B/ST8/02802.

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ROZPOZNAWANIE PISMA ODRĘCZNEGO

W INTELIGENTNYCH SYSTEMACH PROJEKTOWANIA

S t r e s z c z e n i e

W artykule przedstawiono wybrane prace badawcze dotyczące podstaw budowy inteligent-

nych systemów interakcji do projektowania elementów i zespołów maszyn na podstawie ich cech

opisywanych w języku naturalnym. Zaproponowano nową metodę rozpoznawania pisma odręcz-

nego, w której wykorzystano geometryczne cechy znaków. Artykuł dotyczy rozpoznawania izo-

lowanych znaków pisma odręcznego za pomocą sieci neuronowych. W wyniku analizy geome-

trycznej otrzymuje się reprezentacje graficzne rozpoznawanych znaków w postaci opisów wzor-

ców pojedynczych znaków. Wybrane parametry znaków stanowią wejścia sieci neuronowej do

rozpoznawania pisma niezależnego od kroju. W artykule przedstawiono nową metodę rozpozna-

wania pisma naturalnego, a także opisano badania i podano wnioski wynikające z eksperymentów.

Słowa kluczowe: interakcja człowiek–maszyna, inteligentne zautomatyzowane projektowanie,

inteligentny system, rozpoznawanie pisma odręcznego, sieci neuronowe,

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