Eindhoven University of
Technology
Faculty of Architecture,
Building and Planning
Design Systems
In this paper, we present a
theoretical study on automated understanding of the design drawing. This can
lead to design support through the natural interface of sketching. In earlier
work, 24 plan-based conventions of depiction have been identified, such as
grid, zone, axial system, contour, and element vocabulary. These are termed
graphic units. Graphic units form a good basis for recognition of drawings as
they combine shape with meaning. We present some of the theoretical questions
that have to be resolved before an implementation can be made. The contribution
of this paper is: (i) identification of domain knowledge which is necessary for
recognition; (ii) outlining combined strategy of multi-agent systems and online
recognition; (iii) functional structure for agents and their organisation to
converge on sketch recognition.
Key words: Multi-agent
system, decision tree, pattern recognition, sketch
During the design process,
roughly three classes of graphic representations are utilised: the diagram, the
design drawing, and the sketch. The diagram is a clear, well-structured
schematic representation of some state of affairs. It is typically used in the
early phase of design, very often as an analytical tool. The design drawing is
a well-drafted comprehensive drawing applying the techniques of plan, section,
and façade. It is typically utilized after concept design, to communicate ideas
with the patron or other participants in the design process. The sketch is a
quickly produced rough outline of a design idea, typically created in the early
phase of design. Within each class there are many differences due to production
technique, time constraints, conventions of depiction and encoding, and
personal style.
The diagram and sketch are
very apt for early design. They enable fast production of numerous ideas,
thereby allowing the architect to engage in an iterative process using internal
and external memory, reflection, exploration, and test [17], [40]. In this work
we are concerned with computational interpretation of such drawings. We are
motivated from a design support and design research perspective. Much of the
output in early design is produced by means of diagrams and sketches.
Understanding these drawings supports early design assessment, can provide
relevant knowledge, aid in information exchange, and facilitate transfer of
information between design phases or applications.
In order to achieve
understanding of graphic representations, we need a theory for computational
interpretation of drawings. Such a theory is lacking in the design research
field ([23], pp. 174-175; [41], pp. 520; [11], pp. 6) and in general
recognition research [37], [28], pp. 23. An additional complication is the
general oversight of context [36] and annotations [27], pp. 432-433, the lack
of which leads to unrealistic assumptions about image recognition. As a
consequence, we have to draw our foundations, evidence, and intuitions from
many disciplines including design research, image and pattern recognition,
handwriting recognition, artificial intelligence, and multi-agent systems. The
contribution of this paper is: (i) identification of domain knowledge which is
necessary for recognition; (ii) outlining combined strategy of multi-agent
systems and online recognition; (iii) functional structure for agents and their
organisation to converge on sketch recognition.
Design sketches have been
subject of much study. Although there is now general agreement that the sketching
activity is structured [20], [35], there is no consensus in terminology and
framework how to categorise this structuring. This seems to be mainly caused by
the great variety of research questions, design domains, and agendas pursued
(compare for example [17], [32], [27], [34], [25], [5], [38]). Most authors
employ their own categorisation to differentiate between kinds of drawing (e.g.
[17], pp. 128-136, [41], and [27]).
Applied research on
sketch recognition in the engineering areas has focused almost exclusively of
the construction or identification of three-dimensional objects based on
sketches or line drawings (e.g. [19], [10], [38], [31], [7]). With regard to
formalised content of architectural sketch drawings, most work has been done on
the plan representation. Koutamanis [21] outlines a computational analysis of
space formation in architectural plans. Cha and Gero [8] formalise a number of
shape configurations that may occur without consideration of design content. Do
[13] and [18] deal with graphic shorthands of for example ‘table,’ ‘chair,’ and
‘house.’ Koutamanis and Mitossi [22] focus on local and global co-ordinating
devices such as ‘door in wall’ and ‘proportion system’ of Palladio. Leclercq [24]
has implemented a system that takes sketch input and recognises grids, spaces,
and functions. In earlier work, we have classified 24 conventions that are used
in drawings such as ‘grid,’ ‘zone,’ ‘contour,’ and ‘axial system’ [1].
1.1 Basic assumptions
Domain knowledge is important
for drawing recognition as it informs what should be looked for. Based on the
authors above, we can state that the kind of knowledge we need combines meaning
with shape. Such meaning necessarily is based on agreement or convention within
a domain: it is not intrinsic to a graphic representation. In our work, we have
the following basic assumptions:
§
There is nothing inherently
ambiguous in graphic representations. Ambiguity through multiple interpretation
is what the architect does with the graphic representation, not the graphic
representation itself.
§
We primarily consider sketches that
are made with some care for clarity (and which can thus be verified by an
outside observer). Sketches that are purposefully unclear fall outside our
scope. Within all possible conventions of depiction, we only look at plan
representations.
§
Although designers habitually
reinterpret sketches, they do so in an orderly fashion and in a limited way.
Reinterpretations do not wildly diverge and are relatively close to each other.
The question therefore, is not to investigate why sketches are ambiguous, but rather
which clues architects employ and which interpretations they allow.
§
We limit our definition of
computational interpretation of a sketch to instrumental meaning (what do the
graphic entities map to the design task), not architectural theoretical meaning
or other domains of discourse. Instrumental meaning is domain-specific and
context dependent. We take the previously identified 24 graphic units as
instrumental meaning in graphic representations.
1.2 Graphic units
The definition of a graphic
unit is: “a specified set of graphic entities and their appearance that has a
generally accepted meaning within the design community” [1], pp. 22. The appeal
to ‘generally accepted meaning,’ even though it introduces methodological
problems, is necessary because meaning is in many cases the only distinguishing
factor between otherwise similar shapes (e.g. “is this set of straight lines a
grid or a close packing of squares,” and “is this rectangle a table or a
column?”) Graphic units can be divided in structuring and descriptive graphic
units.
Structuring graphic units
build up and organise the design. Their elements only indirectly map on
elements in the built environment, and they are typically left out in final
documentation drawings. They are measurement device, zone, schematic
subdivision, modular field, grid, refinement grid, tartan grid, structural
tartan grid, schematic axial system, axial system, proportion system, and circulation
system.
Descriptive graphic units map
to objects in the built environment: simple contour, contour, specified
form, elaborated structural contour, complementary contours, function symbols,
element vocabulary, structural element vocabulary, combinatorial element
vocabulary, functional space, partitioning system, and circulation.
Graphic Unit |
Description |
Simple contour |
Regular shape showing an outline. |
Contour |
Any irregular shape showing an outline. |
Measurement device |
Measure for establishing (relative) dimensions. |
Specified form |
Contour with specified dimensions. |
Elaborated structural contour |
Outline with structural detail. |
Complementary contours |
Composition of outlines. |
Function symbols |
Textual indication of function. |
Zone |
Area with specific use or function. |
Schematic subdivision |
Schematic depiction of principal subdivision. |
Modular field |
Irregular subdivision of area along coordinating lines. |
Refinement grid |
Grid with smaller module coordinated in other grid. |
Schematic axial system |
Schematic depiction of organisation of axes. |
Axial system |
Organisation of axes applied to building design. |
Grid |
System of modularly repeating coordinating lines. |
Tartan grid |
Double grid based on two alternating modules. |
Structural tartan grid |
Tartan grid with structural elements. |
Element vocabulary |
Set of simple shapes depicting (interior) elements. |
Structural element vocabulary |
Set of simple shapes depicting structural elements. |
Functional space |
Outline combined with function indicator. |
Partitioning system |
Schematic depiction of more detailed subdivision. |
Proportion system |
Diagram showing how proportions are derived. |
Combinatorial element vocabulary |
Precise relationships between particular elements. |
Circulation system |
Principal layout of circulation. |
Circulation |
Layout of circulation applied to building design. |
Sketch recognition therefore,
translates in our view to recognition of graphic units in a plan-based
diagrammatic drawing or sketch which has been created with some care for
clarity.
Image recognition research
mainly focuses on photo- or video-like sources, or aims to mimic some
functionality of the human visual system. Line art or line drawings are quite
distinct from these kind of sources. In particular handwriting recognition has
received a lot of attention, but in the area of drawings there has not been
much work. Comparatively much effort has been given to automated document
management, in particular in the transfer from paper-based maps to the
electronic format of GIS and CAD [30]. Other work includes the reconstruction
of telephone system manhole drawings [6], conversion of machine engineering
line drawings [43], and symbol detection in architectural drawings [4]. In all
cases the processed sources are the precise class of design documentation
drawings.
Most approaches in image
recognition are built on multiple levels of (similar or mixed) recognisers that
incrementally process or reason about information delivered from lower levels.
A recent trend is to incorporate statistical techniques [15], although there is
concern to which extent this can link to domain knowledge [28]. Questions in
this area are about the often manual tuning of the system, incorporation of
domain knowledge, and the problem of low performance when variations in the
drawings occur. It is to be expected that this latter problem is much larger
when the input material is a sketch drawing. This calls for a strategy which
can deal with much uncertainty in the recognition process.
We propose to combine two
techniques to tackle the question of drawing recognition: (1) multi-agent
approach; and (2) online recognition. We discuss these two strategies in the
context of a hypothetical drawing system that recognises graphic units. The
hypothetical system has the following components: (a) drawing area; (b) drawing
pen (similar to the technique reported in [9]); (c) module for tracking and
segmenting strokes made by the pen; (d) multi-agent module for determining
which graphic units are present in the drawing; (e) visual display for feedback
of d-module.
2.1 Multi-agent approach
It has become increasingly
productive to recast the multiple classifier approach in terms of multi-agent
systems. Although the classifiers remain the same ([29] and [16] point out the
utility of aggregating various classifiers), the approach adds to the conceptual
level a more autonomous role to each classifier; it acknowledges explicitly the
limited capabilities per classifier; and it realises that classifiers (in the
guise of agents) should communicate with other classifiers to settle
ambiguities ([42], [14]). The parallelism inherent in multi-agent systems is
another major motivation. In particular when multiple interpretations are
possible, resolution critically depends on a weighed and balanced exchange of
viewpoints. In sequential processing this may lead to long waiting times for a
decision-module to gather all relevant evidence.
Earlier, we have established a
multi-agent framework that forms the basis for building a drawing recognition
system [3]. An agent in the framework has input, output, and an internal state
and processes that are closed to the outside world. The input part senses the
world environment and receives broadcast messages. The output part manipulates
the world environment and broadcasts messages. Agents operate independently. It
is possible to instantiate any number of agents of a given type. The
multi-agent system is multithreaded, having all the agents run continuously at
the same time. As in this way it is not possible to predetermine in which order
agents perform their actions, the design of the agents’ behaviour has to
anticipate various orders. We establish implicit control through the
broadcasts. An agent reads the broadcasts and selects those messages that are
relevant. The agent’s implementation is basically as follows:
Wait for a message (waiting state).
If the message is not interesting, remain in waiting
state.
Do something with the message.
Send messages.
Interact with the environment (if the agent can
manipulate).
Return to waiting state.
The functional behaviour of an
agent Ai is described by its properties {Pi,Gi,Fi,Ci,Si}, where P is purpose, G
is goal, F is a set of features, C is a set of criteria, and S is a set of
segmentation measures. They are informally defined as follows.
The purpose Pi of an agent Ai
is to recognise one particular graphic unit. Thus, there is a Grid-agent,
Zone-agent, Circulation System-agent, Simple Contour-agent, etc.
The goal Gi of an agent Ai is
to determine whether the graphic unit Pi is present in the current state of the
drawing. Thus, the Grid-agent continuously checks for grids.
Each agent Ai employs a set of
features Fx to decide if graphic unit Pi occurs. Thus, the Grid-agent looks for
occurrences of parallel (F1) aligned (F2) straight lines (F3) drawn
consecutively (F4) in the same direction (F5) of roughly the same length (F6)
in two directions (F7) in which the parallel lines keep the same distance from
each other (F8), and the lines in two directions overlap each other in an
implied grid area (F9). Obviously, multiple agents can use the same features.
Each agent Ai has a set of
criteria Cy which establish a relative measure RMi of the degree to which
graphic unit Pi is detected. Thus, the Grid-agent wants to detect at least
three consecutive lines that fulfil F1 - F9 before it considers the possibility
that a grid occurs. If the number of consecutive lines increases that fulfil F1
– F9, the relative measure also increases according to an S-shaped curve.
Each agent Ai has a set of
segmentation measures Sz to determine if it should continue tracking the
current sequence of strokes graphic unit Pi. This basically defines the
activation window during which the agent considers the current sequence of
strokes. Thus, the Grid-agent stops tracking a series of consecutive lines when
curved lines occur, or when new lines no longer fall within the implied grid
area without extending it, and so forth.
2.2 Online recognition
Online recognition means that
computer interpretation takes place while the designer is drawing. In
particular in the handwriting recognition area, numerous researchers opt for
online recognition of text ([44] contains many examples), motivated especially
by the high efficiency of the stroke direction feature ([26], pp. 2271). To the
best of our knowledge, there is no online computer interpretation of sketches
applied in architectural design, and only sporadically in engineering design ([12]
and [33] are notable exceptions). This is an omission since the creation order
of strokes and their direction information can only be derived from an online
process. Especially in the case of diagrams and sketches, where the appearance
of elements shows high degree of variety, these are important clues to derive
what is being drawn (e.g. features F4 and F5 of the Grid-agent example).
The c-module in the
hypothetical sketch system creates a data stream of strokes which forms the
input for the multi-agent system in the d-module. The agents continuously parse
the stream in the manner described above; this is in fact their way of ‘seeing’
the drawing. An agent Ai annotates the drawing by placing four kinds of markers
in the stream: Ms, Mh, Me, and Mc. The start marker Ms designates the first
stroke of a sequence that an agent is tracking. A new start marker can only be
placed after the agent has placed an end marker or claim marker. The hypothesis
marker Mh designates the end of a sequence while the agent is anticipating that
in the near future it will be completed (in this manner, interfering
non-fitting strokes can be ignored). The end marker Me designates the last
stroke of a sequence that an agent is tracking. A new end marker can only be
placed after the agent has placed a start marker. The claim marker Mc is placed
once the agent reaches the threshold value and a graphic unit has been
identified.
At any point in the stream,
any agent can place its own marker; each marker is associated with the agent
that has put it there. After parsing and annotating the data stream, the stream
is stored for later reference.
2.3 Resolving the decision process
Whenever an agent reaches its
threshold value and places a claim, all other agents respond by polling their
current relative measures RMi. In order to converge to a decision, we adopt a
decision tree developed earlier for architects to decide which graphic unit is
present [2]; see Figure 1.
In the decision tree, the path
ABDGK17 is the series of questions that must be answered to determine the
graphic unit. These are currently stated in natural language: A. Is it a
graphic or symbol element; B. Is it a closed shape or a set of one or more
lines; D. Is it a coordinating system or not; G. Is it a zone, grid, or
proportion system; and K. Is it a modular field, grid, refinement grid, tartan
grid, or structural tartan grid. The leaves 1-27 are the specific graphic units.
The decision tree essentially
divides graphic units in groups: text (A7); multiple shapes on building level
(ABCEI); multiple shapes on element level (ABCEJ); single shapes on building
level (ABCF); area structuring devices (ABDG and ABDGK); and building
structuring devices (ABDH, ABDHL, ABDHM, ABDHN). The groups are distinguished
from each other through the nodes A-N. The decision in each node to decide
between one or the order further branch is made on a specific set of features
for that node. Therefore, each graphic unit is characterised by a unique
decision cluster of features (the aggregation of the nodes leading to that
graphic unit).
At any given point during the
online recognition process, it is likely that multiple agents have a relative
measure RMi which is higher than 0. In other words, features are likely not to
activate one single agent (restricted to a single path in the decision tree),
but multiple agents (distributed over the nodes of the whole tree). So we can
view the distribution of the features over the 24 distinct paths as additional
evidence for a ranking which can modify a preliminary ranking based on RMi
only. Qualitatively speaking, the path which has the greatest collection of
features presents the most likely candidate. We still have to determine a
quantitative means of aggregating the features along a path and balancing this
measure against RMi.
Fig. 1. Decision Cluster ABDGK For Graphic Unit Tartan Grid
We have outlined a multi-agent
system for online recognition of graphic units in diagrammatic and sketch
drawings. Empirical work is still needed to calibrate the stroke segmentation
of the c-module. The utility of the decision tree is to separate the major
groups, and show near the leaves how ambiguity occurs through alternative
interpretations. The different depths of the decision tree give a relative
indication which groups are easier to recognise than others. A major step we
still have to take is the formalisation into features of the natural language
questions in the nodes A-N. This also requires an additional revision of the
decision tree into truly binary branches per node. Based on the resulting pool
of features, we can determine which aggregation policy has the most potential.
A running implementation will
provide feedback about the viability of the theoretical work. Supposing that we
find some virtue in the current work, further theoretical questions then
concern whether an agent should keep a record of its own performance when
recognition has to take place; if it is desirable to have each agent segment
the input by itself (and how to aggregate over feature activations that are
built on different segmentations); and whether an agent should follow multiple
tracks in the data stream.
[1] J.A.Hartigan, 'Clustering Algorithms', Wiley, New
York, 1975.
[1] H.H. Achten, ‘Generic Representations’, PhD-diss.,
Eindhoven University of Technology, 1997.
[2] H.H. Achten, Design case retrieval by generic
representations. ‘Artificial Intelligence in Design ’00,’ ed. J.S. Gero, pp. 373-392. Kluwer Academic
Publishers, Dordrecht, 2000.
[3] H.H. Achten and J. Jessurun, Learning from mah
jong. ‘Digital Design: Research and Practice,’ ed. M.L. Chiu et al., pp. 115-124.
Kluwer Academic Publishers, Dordrecht, 2003.
[4] C. Ah-Soon and K. Tombre, Architectural symbol
recognition using a network of constraints, ‘Pattern Recognition Letters’ 22,
pp. 231-248, 2001.
[5] O. Akin and H. Moustapha, Strategic use of
representation in architectural massing, ‘Design Studies’ 25(1), pp. 31-50,
2004.
[6] J.F. Arias, Lai, C.P., Surya, S., Kasturi, R. and
A. Chhabra, Interpretation of telephone system manhole drawings, ‘Pattern
Recognition Letters’ 16, pp. 355-369, 1995.
[7] O. Bimber, Encarnação, L.M. and A. Stork, A
multi-layered architecture for sketch-based interaction within virtual
environments, ‘Computers & Graphics’ 24(6), pp. 851-867, 2000.
[8] M.Y. Cha and Gero, J.S., Shape pattern
recognition, ‘Artificial Intelligence in Design’98,’ ed. Gero, J.S. and F.
Sudweeks, pp. 169-187, Kluwer Academic Publishers, Dordrecht, 1998.
[9] N.Y.-W., Cheng, Stroke sequence in digital
sketching. ‘Architecture in the Network Society,’ ed. Rüdiger, B., Tournay, B.
and H. Ørbæk, pp. 387-393, The Royal Danish Academy of Fine Arts,
Copenhagen, 2004.
[10] M.C. Cooper, Interpreting line drawings of curved
objects with tangential edges and surfaces, ‘Image and Vision Computing’ 15,
pp. 263-276, 1997.
[11] D.W. Dahl, Chattopadhyay, A. and G.J. Gorn, The
importance of visualization in concept design, ‘Design Studies’ 22(1), pp. 5-26,
2001.
[12] C.G.C. van Dijk and A.A.C. Mayer, Sketch input
for conceptual surface design. ‘Computers in Industry’ 34(1), pp. 125-137, 1997.
[13] E. Do, Gross, M.D., Neiman, B. and G. Zimring,
Intentions in and relations among design drawings, ‘Design Studies’ 21(5), pp. 483-503,
2000.
[14] M. van Erp, Vuurpijl, L. and L. Schomaker, An
overview and comparison of voting methods for pattern recognition, ‘Proceedings
of IWFHR’02,’ ed. Williams, A.D., pp. 195-200, IEEE Computer Society, Los
Alamitos, 2002.
[15] D. Forsyth, An empirical-statistical agenda for
recognition, ‘Shape, Contour and Grouping in Computer Vision,’ ed. Forsyth,
D.A., Mundy, J.L., di Gesú, V. and R. Cipolla, pp. 9-21, Springer Verlag,
Berlin, 1999.
[16] G. Giacinto and F. Roli, An approach to the
automatic design of multiple classifier systems, ‘Pattern Recognition Letters’
22, pp. 25-33, 2001.
[17] V. Goel, ‘Sketches of Thought,’ The MIT Press,
Cambridge, 1995.
[18] M. Gross, The Electronic Cocktail Napkin - a computational
environment for working with design diagrams, ‘Design Studies’ 17(1), pp. 53-69,
1996.
[19] H. Jansen and F.-L. Krause, Interpretation of
freehand drawings for mechanical design processes, ‘Computers & Graphics’
8(4), pp. 351-369, 1984.
[20] M. Kavakli, Scrivener, S.A.R. and L.J. Ball,
Structure in idea sketching behaviour, ‘Design Studies’ 19(4), pp. 485-517,
1998.
[21] A. Koutamanis, ‘Development of a Computerized Handbook
of Architectural Plans,’ PhD. diss., Delft University of Technology, 1990.
[22] A. Koutamanis and V. Mitossi, On representation, ‘Design
Research in the Netherlands 2000,’ ed. H. Achten, B. de Vries, and J. Hennessey,
pp. 105-118, Eindhoven: Eindhoven University of Technology, 2001.
[23] B. Lawson and S.H. Loke, Computers, words and
pictures, ‘Design Studies’ 18(2), pp. 171-183, 1997.
[24] P.P. Leclercq, Programming and assisted
sketching. In ‘Computer Aided Architectural Design Futures 2001,’ ed. B. de
Vries and J.P. van Leeuwen and H.H. Achten, pp. 15-31, Kluwer Academic Publishers,
Dordrecht, 2001.
[25] S. Lim, Qin, S.F., Prieto, P., Wright, D. and J.
Shackleton, A study of sketching behaviour to support free-form surface modelling
from on-line sketching, ‘Design Studies’ 25(4), pp. 393-413, 2003.
[26] C.L. Liu, Nakashima, K., Sako, H. and H. Fujisawa,
Handwritten digit recognition: benchmarking of state-of-the-art techniques, ‘Pattern
Recognition’ 36(10), pp. 2271-2285, 2003.
[27] A. McGown, Green, G. and P.A. Rodgers, Visible
ideas: information patterns of conceptual sketch activity, ‘Design Studies’
19(4), pp. 431-453, 1998.
[28] J. Mundy, A formal-physical agenda for
recognition. ‘Shape, Contour and Grouping in Computer Vision,’ ed. Forsyth,
D.A., Mundy, J.L., di Gesú, V. and R. Cipolla, pp. 22-27, Springer Verlag,
Berlin, 1999.
[29] G.S. Ng and H. Singh, Democracy in pattern
classification: combinations of votes in various pattern classifiers, ‘Artificial
Intelligence in Engineering’ 12, pp. 189-204, 1998.
[30] J.M. Ogier, Mullot, R., Labiche, J. and Y.
Lecourtier, Multilevel approach and distributed consistency for technical map
interpretation: Application to cadastral maps, ‘Computer Vision and Image
Understanding’ 70(3), pp. 438-451, 1998.
[31] P. Parodi, Lancewicki, R., Vijh, A. and J.K.
Tsotsos, Empirically-derived estimates of the complexity of labelling line
drawings of polyhedral scenes, ‘Artificial Intelligence’ 105, pp. 47-75, 1998.
[32] A.T. Purcell and J.S. Gero, Drawings and the
design process, ‘Design Studies’ 19(4), pp. 389-430, 1998.
[33] S.-F. Qin, Wright, D.K. and I.N. Jordanov,
On-line segmentation of freehand sketches by knowledge-based nonlinear
thresholding operations, ‘Pattern Recognition’ 34(10), pp. 1885-1893, 2001.
[34] P.A. Rodgers, Green, G. and A. McGown, Using
concept sketches to track design progress, ‘Design Studies’ 21(5), pp. 451-464,
2000.
[35] S.A.R. Scrivener, Ball, L.J. and W. Tseng, Uncertainty
and sketching behaviour, ‘Design Studies’ 21(5), pp. 465-481, 2000.
[36] X.B. Song, Abu-Mostafa, Y., Sill, J., Kasdan, H.
and M. Pavel, Robust image recognition by fusion of contextual information, ‘Information
Fusion’ 3(4), pp. 277-287, 2002.
[37] K. Tombre, Graphics recognition – general context
and challenges, ‘Pattern Recognition Letters’ 16(9), pp. 883-891, 1995.
[38] M. Tovey, Styling and design: intuition and
analysis in industrial design, ‘Design Studies’ 18(1), pp. 5-31, 1997.
[39] M. Tovey, Porter, S., and R. Newman, Sketching,
concept development and automotive design, ‘Design Studies’ 24(2), pp. 135-153,
2003.
[40] I.M. Verstijnen, ‘Sketches of Creative Discovery,’
PhD. diss, Delft University of Technology, 1997.
[41] I.M. Verstijnen, Hennessey, J.M., Leeuwen, C.
van, Hamel, R. and G. Goldschmidt, Sketching and creative discovery, ‘Design
Studies’ 19(4), pp. 519-546, 1998.
[42] L. Vuurpijl and L. Schomaker, L.,Multiple-agent
architectures for the classification of handwritten text, ‘Proceedings of
IWFHR6,’ pp. 335-346, Taejon, Korea, 1998.
[43] L. Wenyin and D. Dori, A generic integrated line
detection algorithm and its object-process specification, ‘Computer Vision and
Image Understanding’ 70(3), pp. 420-437, 1998.
[44] A.D. Williams, ed., Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition, IEEE Computer Society, Los Alamitos, 2002