Methods and apparatus for handwriting recognition

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May 23, 1996 - for a Personal Computer System.” IEEE Transactions on. Assignee: Apple Computer, Inc., Cupertino, CA. Consumer Electronics, vol. CE—28 ...
US006556712B1

(12) United States Patent

(10) Patent N0.: (45) Date 0f Patent:

L0ud0n et al.

(54)

METHODS AND APPARATUS FOR

FOREIGN PATENT DOCUMENTS

HANDWRITING RECOGNITION

(75)

US 6,556,712 B1 Apr. 29, 2003

W0

9608787

Inventors: Gareth H. L0ud0n, Singapore (SG);

3/1996

OTHER PUBLICATIONS

Yi-Min Wu, Singapore (SG); James A. Pittman, Lake Oswego, OR (US)

Yoshida et al. “Online Handwritten Character Recognition for a Personal Computer System.” IEEE Transactions on

(73) Assignee: Apple Computer, Inc., Cupertino, CA

Consumer Electronics, vol. CE—28, No. 3, pp. 202—209, Aug. 1992.*

(US)

_

(*)

_

Notlce?

_

_

_

Sim, Dong—Gyu, “On—Line Recognition Of Cursive Korean

Sub]ect to any dlsclalmer, the term of thls patent is extended or adjusted under 35

Characters Using DP Matching And FuZZy Concept”, vol. 27, NO_ 12, Dec 1, 1994_

U.S.C. 154(b) by 987 days. (List continued on next page.)

(21) Appl' NO‘: 08/652’160 May 23, 1996 (22) Filed;

Primary Examiner—Jon Chang (74) Attorney, Agent, or Firm—Blakely, Sokoloff, Taylor &

(51) (52) (58)

(57)

Zafman LLP

Int. Cl.7 ................................................ .. G06K 9/00

ABSTRACT

US. Cl. ..................................................... .. 382/187

Field of Search ............................... .. 382/187, 188, 382/189

Method and apparatus for handwriting recognition System for ideographies characters and other characters based on subcharacter hidden Markov models. The ideographies char

References Cited

(56)

acters are modeled using a sequence of subcharacter models

and by using two-dimensional geometric layout models of US. PATENT DOCUMENTS

the subcharacters. The subcharacter hidden Markov models

*

are created according to one embodiment by following a set

2,535,323 A * Z1332 5,459,809 A * 10/1995 5,594,810 A 5,675,665 A

5,687,254 A

*

Kim et a1. .

1/1997 Gourdol 10/1997 Lyon ....... ..

........ 382/160 325%?

of design thiygggdczvrit?g lrules. Tlfle hcombéngtion of thed sleqtlenee dand ssheiairtz‘rner m0 6 5 15 use to

382/187

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382/229

* 11/1997 Poon et a1. ............... .. 382/187

7 Claims, 16 Drawing Sheets

Radical Sequence Training Receive digitized input and, for printed characters, interpolate between consecutive strokes to create a

350

one stroke version of the printed character (cursive handwritten characters do not require interpolation)

_/

'

Smooth the interpolated characters

V Scale all inputted characters

(printed and cursive characters ) '

Resample all characters l

Extract representative features (e.g.,A X,A y between consecutive, resampled points)

352

/

354

j 356

J 358

J

7

Train all handwritten characters (for which recognition is

360

desired) after preprocessing characters and extracting _/ representative features; discrete hidden markov radical models are trained for radical sequence recognition

US 6,556,712 B1 Page 2

OTHER PUBLICATIONS

Juang, Yau—Tarng, “On Line Recognition Of Handwritten Chinese Characters: A Syntactic—Sernantic Approach”, Aug. 25—28, 1987, pp. 91—95. K. Lee “Autornatic Speech Recognition; The Development of The SPHINX Syterns”,KluWer, Boston, 1989. Nag, R., et al. “Script Recognition Using Hidden MarkoW Models”, Proceedings of the International Conference on

Tappert, C.C., et al., “The State of The Art in On—Line HandWriting Recognition”. IEEE Transactions on Pattern

Analysis and Machine Intelligence, 01. 12, No. 8, pp. 787—808, 1990. LE. Baurn, “Inequality and Associated MaXirniZation Tech nique In Statistical Estimation of Probabilistic Functions of

Markov processes”, Inequalities, vol. 3, pp. 1—8, 1972. Sato, Y., and K. Kogure, “Online Signature Veri?cation Based on Shape, Motion, and Writing Pressure”, Proceed

Acoustics, Speech and Signal Processing, pp. 2071—2074,

ings, 6th International Converence of Pattern Rec. vol. 6 pp.

1986.

823—826, 1982.

Jeng, B., et al., “On The Use Of Discrete—state Markov Process for Chinese Character Recognition”, SPIE, vol

PariZeau and Plarnondon, Allograph Adjacency Constraints for Cursive Script REconition, Pre—Proceedings IWFHR III, 1993, 252—61. PCT/US97/08796 International Search Report.

1360, Visual Comm. and Image Processing’90, pp. 1663—1670, 1990.

Ng, TM. and LoW, H.B., “Serniautornatic Decomposition and Partial Ordering of Chinese Radicals”, Proceedings of the Int. Conf. on Chinese Cornputing, pp. 250—254, 1988. Mori et al., “Research on Machine Recognition of Hand printed Characters”. IEEE Transactions on Pattern Analysis

and Machine Intelligence, vol. 6, No. 4, pp. 386—405, 1984.

Y.S. Haung and CY. Suen. “An Optirnal Method of Corn

bining Multiple Classi?ers for Unconstrained HandWritten Nurneral Recognition”. Proceedings of the Third Interna tional Workshop on Frontiers in HandWriting Recognition. USA pp. 11—20, 1993. * cited by eXarniner

U.S. Patent

Apr. 29, 2003

Sheet 1 0f 16

US 6,556,712 B1

10

.

12

Define Radicals

f 14 l

‘/



f 16

Create Initial

Create

HMMs

Dictionary

18 \. l t“

m

Perform

HMM training

l

24 Perfonn J

Geometric

Model Training

Create Geometric Models

28

26

~/

¢ _—> .

30

CPen‘orm haracter Recognition



Preclassi?er |

.

29/

Recognized Characters

Fig. 1a

32



U.S. Patent

Apr. 29, 2003

Sheet 2 0f 16

US 6,556,712 B1

Input Stroke Data 37

Pre-classitication

“/

top-n candidate characters 39 for all top-n candidates __/ ‘m’ from pre-classification

i

41

Sub‘character sequence _/

recognition .

Geometric Layout

Recognition

43

_/

4 Combine results from pre-classitication, sub

character sequence recognition & geometric layout recognition 47

i Recognition Results I

Fig. 1b

_./

U.S. Patent

f

Apr. 29, 2003

50

Input

Tablet

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Sheet 4 0f 16

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US 6,556,712 B1

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Final, Cumulative List of

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n-best Probabilities for

Possible lnput Character

¢ Selector

Recognized Character

Fig. 3

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U.S. Patent

Apr. 29, 2003

US 6,556,712 B1

Sheet 5 0f 16

f

150

Input Tablet

r 152

l

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Digitizer and Bus

Digital

interface

Processor

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Memory and Memory Controller

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158a

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Subcharacter Sequence HMM and Layout Training & Recognition Processing

Viterbi Procesing Computer Program Code and Storage

Computer Program

/

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Preprooessing Computer Program Code and Storage

f

158D

158d

Handwriting Input Data

158a

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Subcharacter HMM

Subcharacter Layout

Sequence Memory

Model Memory

Active Portion

Active Portion

f 1 589 Whole Character Pre-Classification

Memory

(Computer Program & Data)

Fig. 4

U.S. Patent

Apr. 29, 2003

Sheet 6 6f 16

Define a set of subcharacters (radicals)

for a particular language

US 6,556,712 B1

-/

l Analyze the radical sequence of every character as written according to the official stroke order; for each

202

radical that is not completed before moving to another _/ radical in the character, separate the radical into smaller radicals so that all radicals can be completed before moving to another radical

l Find every radical that appears in more than one

category (vertical division; horizontal division;

encapsulation; and superimposition) and create one radical per category

__/

l Analyze the number of common ways a character is written in terms of radical order; if the handwritten

206

examples show that a character is commonly written in J more than one (1) way in terms of radical order, then define that entire character as a radical

l Create HMM for each newly defined radical by counting the number of direction changes in pen movement

208

(including those resulting from pen-up changes) when the radical is written; the number of states in the _/ radical's HMM is proportional to the number of direction

changes

l Create lexicon (dictionary) of all characters based

on the sequence of the newly defined radicals

210 _/

_

Flg 5

U.S. Patent

Apr. 29, 2003

Sheet 7 0f 16

US 6,556,712 B1

Fig. 6a (Prior Art)

__ )1, i. 246

Fig. 6b

El, ‘fl @1 249

Fig. 7

250

U.S. Patent

Apr. 29, 2003

Sheet 8 0f 16

US 6,556,712 B1

—\__|___/26° I'_|

262 _./

2653

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282 4 direction changes for radical 244

F

9

(newly defined)

U.S. Patent

Apr. 29, 2003

Sheet 9 0f 16

US 6,556,712 B1

302

Characters

Tree Descrrption

310 ___.E

311D

/

311a-—-- H

.

3123

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312a —~~ 391i

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304

Characters 320 ___ ‘.53

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308

Fig. 10

U.S. Patent

Apr. 29, 2003

Sheet 10 0f 16

US 6,556,712 B1

Radical Sequence Training Receive digitized input and, for printed characters, interpolate between consecutive strokes to create a

one stroke version of the printed character (cursive handwritten characters do not require interpolation)

l Smooth the interpolated characters

l Scale all inputted characters

(printed and cursive characters )

l l

Resample all characters

Extract representative features (e.g., A X, A y

between consecutive, resampled points)

l Train all handwritten characters (for which recognition is

desired) after preprocessing characters and extracting _/ representative features; discrete hidden markov radical models are trained for radical sequence recognition

Fig. 11

U.S. Patent

Apr. 29, 2003

Sheet 12 0f 16

US 6,556,712 B1

Radical Sequence Recognition Receive digitized input and, for printed characters, interpolate between consecutive strokes to create a

400 /

one stroke version

l Smooth the interpolated characters

402

-/

l Scale all inputted characters

404

__/

(printed and cursive characters )

l Resample all characters

4%

_/

l

Extract representative features (e.g.,A X,A y between consecutive, resampled points) 410 Radical sequence recognition - use viterbi algorithm to i/

search lexical tree representation of the radical HMMs

l Detennine list of n-best candidiate characters in the active dictionary based on the results of Viterbi

412

algorithm (this produces an n-best list of probabilities _/ for n-best candidate characters based on radical

sequence recognition)

i For each candidate character in the active dictionary,

multiply Pseq X Play X Ppreclass (where Pseq i8 probability from sequence recognition results for the

candidate character, Play is probability from layout

414 _/

recognition results for the candidate character, and

Pprecl‘ass) is the probability from the whole character prec assi ier to provide a final probability for the candidate character and select the candidate character with the

highest final probability as the recognized character

lg. 15

U.S. Patent

Apr. 29, 2003

Sheet 13 0f 16

US 6,556,712 B1

Geometric Layout Training For each radical in a particular character, obtain n

handwritten examples. Preprocess digitized input

425

examples (interpolate; smooth; scale and resample). / Viterbi segments into radicals

i For each such example (“i”) of the radical in the

427 -/

character calculate; mean Xi, mean Yi, var Xi, var, Y]

i Determine and store statistics for n handwritten examples of the radical:

mean (meanXi...meanXn), var (meanXi...meanXn)

429

_/

mean (meanYi...meanYn), var (meanYi...meanYn) mean (varXi...varXn), var (varXi...VarXn) mean (varYi...varYn), var (varYi...VarYn) (This provides 4 gaussian distributions which are stored

and which describe the radical in the particular character)

l Repeat for next radical in particular character until

61 _/

radicals for the particular character processed

' 43a Flepeat for next character until all characters (for which __/

recognition is desired) are trained for layout recognition

Fig. 16

U.S. Patent

Apr. 29, 2003

Sheet 14 0f 16

printed

454i

US 6,556,712 B1

cursive

456

' /%5

j

462

450 452/? X

480

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X kmji

478a

Fig. 17b 244

245

U.S. Patent

Apr. 29, 2003

Sheet 15 0f 16

US 6,556,712 B1

Geometric Layout Recognition Preprocess input character (interpolate; smooth; scale; and resample)

r Extract sequence features ( A X, Ay) Perform Viterbi search through radical sequence HMMs

(search is limited to characters selected by pre

classifier (“active characters”))

r Segment character into radicals defined in system/dictionary of active characters

v Extract geometric features (meanXi, meanYi, varXi, varYi) for each radical inputted Map extracted geometric features from each inputted radical to 4 gaussian distributions of each radical of the active characters in the dictionary to produce 4 probabilities for each radical in the active dictionary

510

l Multiply 4 probabilities for each radical in the active dictionary to obtain 1 probability value for each such radical

Calculate the average probability for all radicals in the character, determine such average probability for all characters in the active dictionary

l Rank, by average probability of each character, the candidate characters in the active dictionary into n-best list of candidate characters based on this layout

recognition process

Fig. 18

512

U.S. Patent

Apr. 29, 2003

Sheet 16 0f 16

1.0 -

US 6,556,712 B1

1.0 ——

Prob.

Prob.

var (mean Xi...mean Xn)

560

var (mean Yi...mean Y")

5lamearmj

(meanXi...meanXn)

I

mean (meanYi...meanYn)

Pmb

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Fig. 19

US 6,556,712 B1 1

2

METHODS AND APPARATUS FOR HANDWRITING RECOGNITION

Computeing, pp. 250—254 (1988). Ng and LoW designed a semiautomatic method for de?ning Chinese radicals. HoWever, these radicals are not suitable for on-line hand

Writing character recognition using hidden Markov models FIELD OF THE INVENTION

for several reasons. First, to perform on-line character

recognition using radical HMMs, a character model based

The present invention relates to the ?eld of handwriting

on several radical HMMs should be formed from a time

recognition systems and methods for handwriting recogni tion. More particularly, in one implementation, the present invention relates to recognition of on-line cursive handWrit

ing for ideographies scripts.

10

division; horiZontal division; encapsulation and superimposition, and a radical as de?ned by Ng and LoW can appear in more than one of these categories. This has the

BACKGROUND OF THE INVENTION

The Chinese and Japanese languages use ideographies scripts, Where there are several thousand characters. This

large number of characters makes the entry by a typical computer keyboard of a character into a computer system cumbersome and sloW. A more natural Way of entering ideographies characters into a computer system Would be to use handWriting recognition, and particularly automatic rec ognition of cursive style handWriting in a “on-line” manner.

effect of having up to four different shapes and siZes for the 15

r1es.

While the use of subcharacters or radicals to recogniZe 20

tive to each other in a character. In a prior approach by Lyon,

characters; the requirement that the handWriting be printed

the use of a siZe and placement model for subcharacters in 25

a ideographies script has been suggested. See, US. patent application Ser. No. 08/315,886, ?led Sep. 30, 1994 by Richard F. Lyon, entitled “System and Method for Word Recognition Using SiZe and Placement Models.” This method uses the relationship betWeen sequential pairs of

30

subcharacters in a character to create a siZe and placement

on-line cursive style handWriting character recognition. The complexity of the ideographies characters and the character distortion due to non-linear shifting and multiple

styles of Writing also makes character recognition dif?cult, particularly for on-line systems.

model. The subcharacter pair models are created by ?nding the covariance betWeen bounding box features of subchar

one method Which has been used extensively to deal With

the types of problems arising from ideographies character recognition is hidden Markov modeling (HMM). HMMs can deal With the problems of segmentation, non-linear shifting and multiple representation of patterns and have been used extensively in speech and more recently character recogni tion. See, for example, K. Lee “Automatic Speech Recog nition; The Development of The SPHINX System”, KluWer,

Boston, 1989.; Nag, R., et al. “Script Recognition Using

acter pairs. This method relies on the pen lift Which occurs

betWeen subcharacters of ideographies characters and thus is 35

Thus the prior art While providing certain bene?ts for handWriting recognition does not efficiently recogniZe cur 40

sively Written ideographies characters in an on-line manner

(for example, in an interactive manner). Moreover, the use of an HMM for a radical having various categories has a detrimental effect upon the accuracy of the HMM proce

Discrete State Markov Process for Chinese Character

dures. Thus it is desirable to provide improved on-line 45

Image Processing ‘90, pp. 1663—1670, (1990). Jeng used HMMs for off-line recognition of printed Chinese charac ters. In this system described by Jeng, one HMM is used for every Chinese character, and the HMMs are of ?xed topol ogy. The limitations of this approach are that the system can

only useful for printed ideographies characters and cannot be used for cursively Written ideographies characters Where there is usually no pen lift betWeen characters.

Hidden Markov Models”, Proceedings of the International Conference on Acoustics, Speech and Signal Processing, pp. 2071—2074, 1986; and Jeng, B., et al., “On The Use Of

Recognition”, SPIE, vol. 1360, Visual Communications and

ideographies characters is in some Ways desirable, it does

not alWays accurately recogniZe characters Without also recogniZing the geometric layout of the subcharacters rela

HoWever, prior on-line handWriting recognition methods

These prior methods have not been successful at adapting to

radical and this Will have a detrimental effect on the hidden

Markov modeling accuracy because the model has to deal With up to four different basic patterns for the four catego

have concentrated on print style handWritten ideographies is still too sloW for a typical user of a computer system.

sequence of subcharacters, Which Was not done by Ng and LoW. Secondly, Ng and LoW break doWn the characters into four basic constructs or categories of radicals; vertical

recognition of cursive handWriting for ideographies scripts. SUMMARY OF THE INVENTION

The present invention, in one embodiment, creates an 50

on-line handWriting recognition system for ideographies

only recogniZe printed Chinese characters and not cursively

characters based on subcharacter hidden Markov models

Written characters. This recognition system also requires a

(HMMs) that can successfully recogniZe cursive and print style handWriting. The ideographies characters are modeled using a sequence of subcharacter models (HMMS) and they are also modeled by using the tWo dimensional geometric

large amount of memory to store the thousands of character

level Markov models. Another disadvantage of the system is that a ?xed topology is used for every character and the number of states for a character’s hidden Markov model does not depend on the complexity of the character.

55

layout of the subcharacters Within a character. The system includes, in one embodiment, both recognition of radical

In ideographies languages, such as Chinese, the thousands

sequence and recognition of geometric layout of radicals

of ideographies characters can be broken doWn into a smaller set of a feW hundred subcharacters (also referred to as radicals). There are several Well dictionaries Which de?ne

Within a character. The subcharacter HMMs are created by 60

of the subcharacter models is used to recogniZe the hand Written character. Various embodiments of the present

recogniZed radicals in the various ideographies languages. Thus, the thousands of ideographies characters may be

invention are described beloW.

represented by a smaller subset of the subcharacters or

radicals. See, Ng, T. M. and LoW, H. B., “Semiautomatic Decomposition and Partial Ordering of Chinese Radicals”, Proceedings of the International Conference on Chinese

folloWing a set of design rules. The combination of the

sequence recognition and the geometric layout recognition

65

In one embodiment of the present invention, a method of recogniZing a handWritten character includes the steps of comparing a handWritten input to a ?rst model of a ?rst

US 6,556,712 B1 3

4

portion of the handwritten character and comparing the

recogniZed radical and storing a second model in a computer

handWritten input to a second model of a second portion of

readable storage medium for the ?rst recogniZed radical, Where the ?rst recogniZed radical has different shapes

the character, Where the second portion of the character has been de?ned in a model to folloW in time the ?rst portion. In a typical embodiment, the ?rst model is a ?rst hidden Markov model and the second model is a second hidden Markov model Where the second model is de?ned to folloW

depending on the use of the ?rst recogniZed radical in a

category (e.g. horiZontal division or vertical division). While this method increases the storage requirements of a database

according to the present invention, it does improve the accuracy of the HMM techniques used according to the present invention.

the ?rst model in time; typically the ?rst model is processed (eg by a Viterbi algorithm) in the system before the second model such that the system can automatically segment the ?rst portion of the character from the second portion of the character, Which is useful in the geometric layout recogni tion of the present invention. In a typical example, the ?rst portion Will include a ?rst portion of a recogniZed radical and the second portion Will include a second portion of the

Various systems are also described in accordance With the present invention. In a typical example, a system of the present invention includes a handWriting input tablet for

inputting handWritten characters. This tablet is typically 15

same recogniZed radical, Where the ?rst portion is normally Written ?rst and then at least another portion of another

recogniZed radical is Written and then ?nally the second portion is Written. In this manner, the radical HMMs re separated and ordered to preserve the time sequence of the manner in Which the radicals are Written. It Will be appre ciated that the number of radicals per character vary from one to many (e.g. up to 10 radicals per character).

According to another aspect of the present invention, a method of the present invention for recogniZing a handWrit ten character includes the steps of comparing a ?rst geo

processor Will perform the recognition procedures through a 25

before proceeding to the hidden Markov states of the second model. Various systems of the present invention may be

niZed to a ?rst geometric model of the ?rst portion, and comparing a second geometric feature of a second portion of a character to a ?rst geometric model of the ?rst portion. In

implemented, including a system in auxiliary hardWare

a typical embodiment, this process of recogniZing the layout

Which may reside in a printed circuit board card in an

expansion slot of a computer system. Alternatively, the

present invention may be practiced substantially in software by storing the necessary databases, data and computer 35

character by use of a Viterbi search through a lexical tree of

hidden Markov models, Which include models of the ?rst

and second radicals. This segmentation alloWs the layout recognition system to selectively obtain a geometric feature of a ?rst portion of a character Which is then used to compare to a geometric model of the ?rst portion as Well as other

data and the databases stored in the memory to perform in

the handWriting character recognition according to the present invention.

system. 45

The present invention also includes computer readable

storage media (eg a hard disk, optical disk, etc.) Which store executable computer programs and data Which are used

to perform the handWriting recognition processes according

invention, a method of creating a database of radicals for use

to the present invention. This storage media typically loads (through control of the processor) a system memory (e.g. DRAM) With the computer programs and databases Which are used for the handWriting recognition.

in a handWriting recognition procedure is provided. This method includes storing a ?rst model in a computer readable storage medium for a ?rst portion of the character to be

recogniZed, and storing a second model in the computer readable storage medium for a second portion of the character, Wherein the ?rst portion comprises a ?rst portion of a recogniZed radical and a second portion comprises a

programs in a general purpose memory and/or computer readable media (eg hard disk) Which is a main memory of a computer system. This main memory is coupled to a processor Which is the main processor of the computer system so that the processor may execute the computer programs stored in the memory in order to operate on the

portions of geometrically trained and modeled radicals in the The present invention comprises various methods and systems for de?ning the databases and dictionaries Which are used in the handWriting recognition processes of the present invention. According to one aspect of the present

lexical tree of HMMs stored in the memory using a Viterbi

algorithm and Will perform the recognition on the ?rst model

metric feature of a ?rst portion of a character to be recog

of the radicals of a character is performed in conjunction With the recognition of the time sequence of the radicals of the character. Typically, the recognition of the time sequence of radicals provides the segmentation of the handWritten

coupled to a bus Which receives the input of the handWritten character from the tablet. Typically, a processor is coupled to this bus and a memory is also coupled to this bus. The memory stores the various databases and computer pro grams described according to the present invention. In a typical embodiment, the memory stores a ?rst model of a ?rst portion of a character to be recogniZed and stores a second model of a second portion of the character, Where the memory stores the second model such that the second model is de?ned to folloW in time the ?rst model. Typically, the

BRIEF DESCRIPTION OF THE DRAWINGS 55

second portion of the same recogniZed radical, Where the ?rst portion is normally Written ?rst and then at least another portion of another recogniZed radical is Written and ?nally the second portion is Written. While this increases the storage requirements for storing the radicals because several radicals may be created from a single recogniZed radical, recognition of radical sequence is noW permissible accord ing to the present invention. According to another method of the present invention for

The present invention is illustrated by Way of example and not limitation in the ?gures of the accompanying draWings, in Which like references indicate similar elements. FIG. 1A is ?oWchart shoWing the overall methods of the present invention and hoW the different processes are used

for training and recognition and hoW they are interrelated and interconnected. FIG. 1B illustrates in further detail the methods and steps

of the recognition procedures of the present invention and the interrelationship betWeen those procedures.

recognition, a method includes the steps of storing the ?rst

FIG. 2 shoWs a typical implementation of a general purpose computer system Which may utiliZe the present

model in a computer readable storage medium for a ?rst

invention and be an embodiment of the present invention.

creating a database of radicals for use in handWriting

65