Handwriting Recognition of Pitman's Shorthand - School of

239 Pages·2009·4.57 MB·English

by  Computer Science

Lexicon Organisation and
Contextual Methods for Online
Handwritten Pitman’s
Shorthand Recognition
by Swe Myo Htwe, BSc
Thesis submitted to the University of Nottingham for the degree of
Doctor of Philosophy
School of Computer Science and Information Technology
December 2006
To my parents and fiancé
ii
Abstract
This research investigates innovations for the computer transcription of handwritten Pitman’s
Shorthand as a rapid means of text entry (up to 100 words per minute) into today’s pen-based
handheld devices.
Two mathematical models are developed in this work. The first model deals with high level
phonetic-based translation, while the second model is specifically concerned with low level
primitive-based translation. Both models are closely related to the lexicon organization and
contextual processing for online handwritten Pitman’s Shorthandrecognition.
A number of research issues that arise from interpreting handwritten Pitman’s Shorthandstrokes
of digital ink astextare addressed including: (a) a feasibility study into improving a conventional
phonetic-based transliteration approach to advance word recognition; (b) an investigation into
new Bayesian Networkmodelling of strokes and their relationships in order to solve the problem
of geometric variations and vowel ambiguities of handwritten Pitman’s Shorthand; (c) generation
of a new machine-readable Pitman’s Shorthand lexicon to facilitate the direct transcription of
geometric features of Pitman’s Shorthand into English text; (d) analysis of the impact of
statistical language modelling in handwriting phrase recognition; (e) and a discussion of the
graphical user interface issues in relation to the development of a commercial prototype from the
frame of reference of this research.
The research has been carried out in close cooperation with Nanyang Technology University
(NTU) in Singapore. The system is currently undergoing a final evaluation in terms of its
recognition accuracy,as well as its potential to be introduced as a commercially viable fast text
input system.
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Acknowledgements
I would like to take this opportunity to express my sincere gratitude to my supervisor Dr.
Colin Higgins for his valuable guidance and constant support since the day I had stepped
into the School of Computer Science of the University of Nottingham till the day of the
successful completion of this research.
My sincere gratitude also goes to Professor Graham Leedham for his dedicated guidance and
genuine assistance for keeping the close collaboration between the two participating teams
of this research. My deepest thanks also go to Ma Yang for her heartfelt contribution and
her immediate responses during the critical time of this collaborative research.
My sincere thanks also go to Ms. Joyce Cox for her kind and professional help in proof
reading the quality of English of the thesis. Also, from the bottom of my heart, I am very
grateful to all participants who have helped me in the experiments of this research. Many
thanks also go to my colleagues in the LTR Research group for their warm friendship that
made me feel at home in our LTR lab.
Also, my endless thanks to my uncle, Dr. Kyin Win for supporting me financially and
emotionally to make my dream of participating in the doctorate research come true. My
heartfelt thanks also go to the International Office and the School of Computer Science of
the University of Nottingham for their enormous financial support for the development of
this research.
Also, my sincere love and thanks to my parents, fiancé, and all my friends in Nottinghamfor
supporting me financially, emotionally and spiritually during the difficult days of my long
residence in Nottingham.
Last but not least, my sincere thanks to all the members of the School of Computer Science
of the University of Nottingham for all their help and advice, given to me when I neededit
most.
Thank you all, Swe Myo Htwe.
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Table of contents
ABSTRACT …………………………………………………………………………………………..II
ACKNOWLEDGEMENTS…………………………………………………………………………III
TABLE OF CONTENTS …………………………………………………………………………...IV
LIST OF FIGURES ………………………………………………………………………………
LIST OF TABLES ………………………………………………………………………………….
1 LINGUISTIC POST PROCESSING OF HANDWRITTEN PITMAN’S SHORTHAND..1
CHAPTER 1 INTRODUCTION...................................................................................................2
1.1 BACKGROUND......................................................................................................................3
1.1.1 Collaboration.................................................................................................................3
1.1.2 Motivation......................................................................................................................4
1.1.3 Scope..............................................................................................................................5
1.2 BRIEF OVERVIEW.................................................................................................................7
1.2.1 General Objectives and Contributions...........................................................................7
1.3 SYNOPSIS OF THE DISSERTATION.......................................................................................12
2 BACKGROUND TO THE AUTOMATIC RECOGNITION OF HANDWRITTEN
PITMAN’S SHORTHAND...............................................................................................................15
CHAPTER 2 INTRODUCTION.................................................................................................16
2.1 EVALUATION OF EXISTING TEXT INPUT SYSTEMSFOR HANDHELD DEVICES.......................16
2.1.1 On-screen keyboards vs. a handwritten Pitman’s Shorthandrecognizer.....................17
2.1.2 A cursive handwriting recognizer vs. a handwritten Pitman’s Shorthandrecognizer.17
2.1.3 Gesture based text entry systems vs. a handwritten Pitman’s Shorthandrecognizer...18
2.1.4 Speech recognition systems vs. a handwritten Pitman’s Shorthandrecognizer...........19
2.2 PITMAN’S SHORTHAND:A BRIEF OVERVIEW......................................................................20
2.3 AUTOMATIC RECOGNITIONOF HANDWRITTEN PITMAN’S SHORTHAND:AN OVERVIEW......23
2.4 HANDWRITING RECOGNITION ALGORITHMSTO IMPROVE A WORD LEVEL TRANSLITERATION
26
2.4.1 Hidden Markov Models (HMMs).................................................................................27
2.4.2 Neural Networks...........................................................................................................28
2.4.3 Bayesian Networks.......................................................................................................29
2.4.3.1 Conditional independence.................................................................................................30
2.4.3.2 Inference...........................................................................................................................32
2.4.3.3 Learning............................................................................................................................33
2.5 NATURAL LANGUAGE PROCESSING ALGORITHMS FOR HANDWRITTEN PHRASERECOGNITION
35
2.5.1 Statistical language modeling......................................................................................35
2.5.2 Viterbi algorithm..........................................................................................................36
2.6 PEN APPLICATION PROGRAMINTERFACES (APIS)..............................................................37
2.7 SUMMARY..........................................................................................................................38
3 EVALUATION OF PHONETIC BASED TRANSCRIPTION OF VOCALISED
HANDWRITTEN PITMAN’SOUTLINES.....................................................................................39
CHAPTER 3 INTRODUCTION.................................................................................................40
3.1 SYSTEM OVERVIEW............................................................................................................41
3.2 TRANSCRIPTION OF VOCALIZED OUTLINES BASED ON A PHONETIC APPROACH...................43
3.3 LEXICON PREPARATION.....................................................................................................44
3.4 NEAREST NEIGHBOURHOOD QUERY (NNQ)......................................................................47
3.5 FEATURE TO PHONEME CONVERSION................................................................................49
3.6 PHONEME ORDERING.........................................................................................................51
3.7 EXPERIMENTAL RESULTS...................................................................................................54
3.7.1 Data Set........................................................................................................................54
3.7.2 Analysis of a phonetic lexicon......................................................................................55
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3.7.3 Performance evaluation of the word level transcription..............................................57
3.8 DISCUSSION.......................................................................................................................58
4 BAYESIAN NETWORKBASED WORD TRANSCRIPTION...........................................61
CHAPTER 4 INTRODUCTION.................................................................................................62
4.1 SYSTEM OVERVIEW...........................................................................................................63
4.2 SUMMARY OF BAYESIAN NETWORKBASED WORD TRANSCRIPTION...................................64
4.3 LIFE CYCLE OF OUTLINE MODELS.....................................................................................65
4.4 OUTLINE MODEL ARCHITECTURE......................................................................................67
4.4.1 Nodes of an outline model............................................................................................68
4.4.2 Relationships between nodes........................................................................................73
4.5 INFERENCE.........................................................................................................................74
4.5.1 Message Initialization..................................................................................................75
4.5.2 Belief Updating............................................................................................................76
4.6 LEARNING OF OUTLINE MODELS.......................................................................................78
4.6.1 Learning of consonant primitives.................................................................................79
4.6.2 Learning of vowel primitives........................................................................................81
4.7 MODEL SELECTION............................................................................................................82
4.8 EXPERIMENTAL RESULT.....................................................................................................86
4.8.1 Data set........................................................................................................................87
4.8.2 Evaluation of the recognition engine............................................................................89
4.8.3 Evaluation of the word transcription accuracy............................................................93
4.8.4 Analysis of word transcription accuracy using the single consonant data set.............94
4.8.4.1 Analysis of the recognition accuracy vs. the transcription accuracy.................................94
4.8.4.2 Analysis of the accuracy of a result list............................................................................95
4.8.4.3 Analysis of the correction accuracy vs. the classification/vowel errors............................97
4.8.4.4 Analysis of factors influencing the accuracy of a result list..............................................98
4.8.5 Analysis of word transcription accuracy using stroke-combinationdata set...............99
4.8.5.1 Analysis of the recognition accuracy vs. the transcription accuracy.................................99
4.8.5.2 Analysis of the accuracy of a result list..........................................................................100
4.8.5.3 Analysis of the correction accuracy vs. the classification/vowel errors..........................101
4.8.5.4 Analysis of factors influencing the accuracy of a result list............................................102
4.8.6 Analysis of word transcription accuracy for the special-rule data set.......................103
4.8.6.1 Analysis of the recognition accuracy vs. the transcription accuracy...............................103
4.8.6.2 Analysis of the accuracy of the result list.......................................................................104
4.8.6.3 Analysis of the correction accuracy vs. the classification/vowel errors..........................105
4.8.6.4 Analysis of factors influencing the accuracy of a result list............................................106
4.9 DISCUSSION.....................................................................................................................107
5 GENERATION OF A MACHINE-READABLE PITMAN’S SHORTHANDLEXICON
110
CHAPTER 5 INTRODUCTION...............................................................................................111
5.1 OVERVIEW.......................................................................................................................112
5.1.1 Rule-based creation of the electronic Pitman’s Shorthandlexicon............................113
5.2 STRUCTURE OF THE ELECTRONIC PITMAN’S SHORTHANDLEXICON.................................114
5.2.1 Feature set..................................................................................................................114
5.2.2 Key..............................................................................................................................115
5.2.3 Lexicon layout............................................................................................................116
5.3 CONVERSION PROCEDURE................................................................................................118
5.3.1 The importance of algorithms of the presented rules.................................................119
5.3.2 Description of Rules...................................................................................................120
5.4 EXPERIMENTAL RESULTS.................................................................................................127
5.4.1 Data set......................................................................................................................127
5.4.2 Analysis of the accuracy of a machine readable Pitman’s Shorthandlexicon...........128
5.4.3 Analysis of the distribution of homophones in machine-readable Pitman’s Shorthand
lexicons 134
5.5 DISCUSSION.....................................................................................................................136
6 PHRASE LEVEL TRANSCRIPTION OF ONLINE HANDWRITTEN PITMAN’S
SHORTHANDOUTLINES............................................................................................................137
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CHAPTER 6 INTRODUCTION...............................................................................................138
6.1 CONTEXTUAL REJECTION STRATEGY...............................................................................139
6.2 HANDWRITTEN PITMAN’S SHORTHANDPHRASE RECOGNITION........................................141
6.3 THE INTEGRATION OF A PITMAN’S SHORTHANDPHRASE RECOGNISER WITH APIS...........143
6.4 EXPERIMENTAL RESULTS.................................................................................................146
6.5 DISCUSSION.....................................................................................................................146
7 GRAPHICAL USER INTERFACES OF THE HANDWRITTEN PITMAN’S
SHORTHANDRECOGNITION SYSTEM...................................................................................148
CHAPTER 7 INTRODUCTION...............................................................................................149
7.1 OVERVIEW.......................................................................................................................150
7.2 INK DATA COLLECTION IN THIS RESEARCH.......................................................................151
7.3 GENERAL TRAINING DATA COLLECTION TOOL.................................................................155
7.4 DEVELOPER GRAPHICAL USER INTERFACE......................................................................158
7.5 SHORTHAND DATA ENTRY GRAPHICAL USER INTERFACES................................................159
7.6 EXPERIMENTAL RESULTS.................................................................................................164
7.6.1 Analysis of the general distribution of user fondness for the presented prototypes...166
7.6.2 Analysis of the distribution of user fondness for the presented prototypes in the case of
speed writing.............................................................................................................................167
7.6.3 Analysis of the distributionof user fondness for the presented prototypes in the case of
a small amount of text entry into handheld devices..................................................................167
7.6.4 The comparison of the most favourite GUI of experienced shorthand writers and that
of novice shorthand writers......................................................................................................168
7.7 DISCUSSION.....................................................................................................................169
8 CONCLUSION.......................................................................................................................171
CHAPTER 8 INTRODUCTION...............................................................................................172
8.1 RESEARCH WORK SUMMARY............................................................................................172
8.2 CONTRIBUTION................................................................................................................174
8.3 FUTURE WORK.................................................................................................................175
8.3.1 Improvement upon the overall system........................................................................175
8.3.2 Application of the presented system to the real life problems....................................177
8.4 DISSEMINATION...............................................................................................................177
REFERENCES.................................................................................................................................180
APPENDIX..........................................................................ERROR! BOOKMARK NOT DEFINED.
vii
FIGURE 1.1: SCOPE OFTHE THESIS...........................................................................................6
FIGURE 1.2: A HIGH LEVEL VIEW OF THE SCOPE OFTHE RECOGNITION ENGINE
AND THE TRANSCRIPTION ENGINE...................................................................................9
FIGURE 2.1: ILLUSTRATION OF TEXT ENTRY USING SHARKSYSTEM (A) THE WORD
“QUICK” IS WRITTEN USING ATOMIK KEYBOARDLAYOUT (B) THE WORD
“QUICK” IS WRITTEN WITHOUT USING A TEMPLATE KEYBOARD...........................19
FIGURE 2.2: BASIC CONSONANTS OF PITMAN’S SHORTHANDAS ILLUSTRATED IN
[OJ95].........................................................................................................................................21
FIGURE 2.3: /W/, /Y/, H/ CONSONANTS OF PITMAN’S SHORTHAND.....................................21
FIGURE 2.4: VOWELS, DIPHTHONGS AND DIPHONES OF PITMAN’S SHORTHAND..........21
FIGURE 2.5: ILLUSTRATION OF VOCALIZED OUTLINES........................................................22
FIGURE 2.6: (A) BASIC NOTATIONS OF PITMAN’S SHORTHAND(B) THE WORD “PLAY”
IS WRITTEN PHONETICALLY USING BASIC NOTATIONS (C) THE WORD “PLAY” IS
WRITTEN USING A SPECIAL RULE OF PITMAN’S SHORTHAND.................................22
FIGURE 2.7: (A) SAMPLES OF SHORT FORMS (B) SAMPLES OF PHRASES...........................23
FIGURE 2.8: A SAMPLEHMM MODEL FOR A SINGLE OUTLINE OF PITMAN’S
SHORTHAND. AT EACH STATE I, Β PROBABILITY OF A PARTICULAR STROKE S
I I
TO BE TYPE T IS OBSERVED...............................................................................................27
I
FIGURE 2.9: AN INDIVIDUAL CELL AOF NEURAL NETWORK, MODELLED FOR THE
CLASSIFICATION OF HANDWRITTEN PITMAN’S SHORTHANDIN [LQ90]................28
FIGURE 2.10: ILLUSTRATION OF STROKE DEPENDENCIES IN PITMAN’S SHORTHAND
(A) VOWEL DEPENDENCY (B) POSITIONAL DEPENDENCY OF THE FIRST
CONSONANT PRIMITIVE......................................................................................................30
FIGURE 2.11: C IS CONDITIONALLY INDEPENDENT OF W GIVEN R....................................31
FIGURE 2.12: S IS CONDITIONALLY DEPENDENT ON R GIVEN AN OBSERVED
DATA, W...................................................................................................................................31
FIGURE 2.13: ILLUSTRATION OF THE BAYES BALL ALGORITHM [SR98]. IF THERE IS NO
FLOW OF A BALL FROM A TO B IN A GRAPH, A AND B ARE CONDITIONALLY
INDEPENDENT GIVEN A SET OF OBSERVED OR HIDDEN VARIABLES X AND VICE
VERSA.......................................................................................................................................32
FIGURE 3.1: AN ABSTRACT VIEW OF THE WHOLE SYSTEM..............................................42
FIGURE 3.2: DETAILEDVIEW OF A VOCALIZED OUTLINE INTERPRETER......................44
FIGURE 3.3: ILLUSTRATION OF SAMPLE WORDS IN NORMAL ENGLISH AND
PITMAN’S SHORTHAND.......................................................................................................45
Consonant neighbourhoods vowel neighbourhood circle neighbourhood
close circles
at the beginning of an outline
F, V T, D
unclose circles
P,B TH, th in the middle of an outline
R
S, Z hooks
at the end of an outline
FIGURE 3.4: SAMPLE NEIGHBOURHOODS PREDEFINED IN THE NEAREST
NEIGHBOURHOOD QUERY APPROACH.........................................................................47
FIGURE 3.5: SAMPLE OUTPUT PRODUCED BY THE NEAREST NEIGHBOURHOOD
QUERY......................................................................................................................................48
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FIGURE 3.6: SAMPLE OF PHONEME TRANSLATION OF A DOUBLE LENGTH STROKE
....................................................................................................................................................51
FIGURE 3.7: (A) SAMPLE INPUT OF PHONEME ORDERING PROCESS (B)SAMPLE
OUTPUT OF PHONEME ORDERING PROCESS.............................................................52
FIGURE 3.8: SAMPLE ELEMENT OF A PHONETICLEXICON IN A HASH TABLE.............54
FIGURE 3.9: SAMPLE COLLECTED OUTLINES........................................................................55
FIGURE 3.10: THE DISTRIBUTION OF HOMOPHONES IN DIFFERENT SIZED
PHONETIC LEXICONS..........................................................................................................55
FIGURE 3.11: ILLUSTRATION OF THE INCIDENCE OF PHONEMEVARIATION DUE TO
CONFUSION BETWEEN A CIRCLE AND A HOOK..........................................................59
FIGURE 3.12: ILLUSTRATIONOF THE INCIDENCE OF PHONEME VARIATION DUE TO
LENGTH CONFUSION...........................................................................................................60
FIGURE 4.1: AN ABSTRACT VIEW OF THE WHOLE SYSTEM..............................................63
FIGURE4.2: ILLUSTRATION OF BAYESIAN NETWORKBASED WORD
TRANSCRIPTION...................................................................................................................64
FIGURE 4.4ILLUSTRATION OF THREE PAIRS OF SIMILAR OUTLINES GROUPED IN AN
OUTLINE MODEL....................................................................................................................66
FIGURE 4.5LIFE CYCLE OF OUTLINE MODELS........................................................................67
FIGURE 4.6ILLUSTRATION OF DIFFERENT CHRONOLOGICAL WRITING ORDER OF
NORMAL ENGLISH AND PITMAN’S SHORTHAND..........................................................68
FIGURE 4.7 ILLUSTRATION OF UNIQUE NODES OF AN OUTLINE MODEL.........................69
FIGURE 4.8 ILLUSTRATION OF STEP BY STEP CREATION OF OUTLINE MODELS............71
FIGURE 4.9 SAMPLE TRAINING DATA FOR THE WORD “BAKE” PROCESSED BY THE
RECOGNITION ENGINE; THE ITALIC TEXT ON THE RIGHT EXPLAINS WHAT EACH
LINE OF DATA REPRESENTS...............................................................................................72
FIGURE 4.10 ILLUSTRATION OF CONDITIONAL DEPENDENCY OF VARIABLES IN AN
OUTLINE MODEL USING THE BAYES BALL ALGORITHM [SR98]. IF THERE IS NO
FLOW OF A BALL FROM A TO B IN A GRAPH, AAND B ARE CONDITIONALLY
INDEPENDENT GIVEN A SET OF OBSERVED OR HIDDENVARIABLES X AND VICE
VERSA.......................................................................................................................................74
FIGURE 4.11ILLUSTRATION OF OUTLINE MODEL SELECTION STRATEGIES...................86
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FIGURE 4.12: SAMPLES OF THE STROKE COMBINATION DATA SET..............................87
FIGURE 4.13: TWO DIFFERENT SHORTHAND OUTLINES FOR THE WORD “AFTER”; (A)
THE WORD “AFTER” IS WRITTEN ACCORDING TOTHE DIRECT CONVERSION
OF PHONEMES INTO PRIMITIVES (B) THE WORD “AFTER”IS WRITTEN
ACCORDING TO THE DOUBLE-LENGTH RULE OF PITMAN’S SHORTHAND.........88
FIGURE 4.14: SCREEN SHOT OF OUTLINES WRITTEN BY WRITER A.............................89
FIGURE 4.15: EVALUATION OF THE VOCALISED OUTLINE IDENTIFICATION OF THE
RECOGNITION ENGINE........................................................................................................90
FIGURE 4.16: EVALUATION OF THE SEGMENTATION ACCURACY OF THE
RECOGNITION ENGINE........................................................................................................92
FIGURE 4.17: EVALUATION OF THE CLASSIFICATION ACCURACY OF THE
RECOGNITION ENGINE........................................................................................................93
FIGURE 4.18: ILLUSTRATION OF A RELATIONSHIP BETWEEN RECOGNITION
ACCURACY AND TRANSCRIPTION ACCURACY OF THE SINGLE CONSONANT
DATA SET.................................................................................................................................95
FIGURE 4.19: COMPARISON OF THE HANDWRITING OF TWO WRITERS.......................96
FIGURE 4.20: ILLUSTRATION OF THE WORD TRANSCRIPTIONACCURACY OF THE
SINGLE CONSONANT DATA SET......................................................................................96
FIGURE 4.21: ILLUSTRATION OF THE CORRECTION ACCURACY IN COMPARISON
WITH THE CLASSIFICATION OR VOWEL ERRORS OF THE SINGLE CONSONANT
DATA SET.................................................................................................................................98
FIGURE 4.22: ILLUSTRATION OF ANAVERAGE DISTRIBUTIONOF FACTORS
INFLUENCING THE ACCURACY OFA RESULT LIST (SINGLE CONSONANT DATA
SET)...........................................................................................................................................99
FIGURE 4.23: ILLUSTRATION OF THE RELATIONSHIP BETWEEN RECOGNITION
ACCURACY AND TRANSCRIPTION ACCURACY OF THE STROKE-COMBINATION
DATA SET...............................................................................................................................100
FIGURE 4.24: ILLUSTRATION OF THE WORD TRANSCRIPTIONACCURACY OF THE
STROKE-COMBINATION DATA SET................................................................................101
FIGURE 4.25: ILLUSTRATION OF THE CORRECTION ACCURACY INCOMPARISON
WITH THE CLASSIFICATION/VOWEL ERRORS OF THE STROKE COMBINATION
DATA SET...............................................................................................................................102
FIGURE 4.26: ILLUSTRATION OF ANAVERAGE DISTRIBUTIONOF FACTORS
INFLUENCING THE ACCURACY OF A RESULT LIST(STROKE-COMBINATION
DATA SET).............................................................................................................................103
FIGURE 4.27: RELATIONSHIP BETWEEN RECOGNITION ACCURACY AND
TRANSCRIPTION ACCURACY OF THE SPECIAL-RULE DATA SET........................104
FIGURE 4.28: EVALUATION OF THE WORD TRANSCRIPTION ACCURACY OF THE
SPECIAL-RULE DATA SET.................................................................................................105
FIGURE 4.29: ILLUSTRATION OF THE CORRECTION ACCURACY IN COMPARISON
WITH CLASSIFICATION OR VOWEL ERRORS OF THE SPECIAL-RULE DATA SET
..................................................................................................................................................106
FIGURE 4.30: ILLUSTRATION OF ANAVERAGE DISTRIBUTIONOF FACTORS
INFLUENCING THE ACCURACY OF A RESULT LIST (SPECIAL-RULE DATA SET)
..................................................................................................................................................107
FIGURE 5.1:(A) SAMPLE ENTRIES OF A CONVENTIONAL PITMAN’S SHORTHAND
DICTIONARY AVAILABLEIN BOOK FORMAT (B) SAMPLE ENTRIES OF AN
ELECTRONIC PITMAN’S SHORTHANDLEXICON........................................................112
FIGURE 5.2: SAMPLE KEYS OF THEELECTRONIC PITMAN’S SHORTHANDLEXICON;
VOWELS ARE UNDERLINED.............................................................................................115
FIGURE 5.3: SAMPLE ENTRIES OF THE ELECTRONIC PITMAN’S SHORTHAND
LEXICON.................................................................................................................................116
FIGURE 5.4: ILLUSTRATION OF THE CONVERSION PROCEDURE.................................119
FIGURE 5.5: ILLUSTRATION OF THE USE OF A DOT PRIMITIVE FOR THE SOUND COM
AT THE BEGINNING OF A WORD....................................................................................123
FIGURE 5.6: ILLUSTRATION OF THE USE OF NEGATIVE PREFIX IR-IN A VOCALISED
OUTLINE.................................................................................................................................124
FIGURE 5.7: ILLUSTRATION OF THE USE OF PL HOOK IN A VOCALISED OUTLINE..125
FIGURE 5.8: ILLUSTRATION OF A ONE SYLLABLE HALF-LENGTH OUTLINE...............126