Introduction to Vectorization of Engineering Drawings

Song Jiqiang18/9/2001

Concept

• Vectorization: the conversion from a raster image to its vector-form file.

Engineering drawings

in paper form

Raster images

Vector-form(CAD files)

Scan Vectorization

Why do vectorization?

• A lot of old drawings to be reused, and CAD files are more editable than images.

• Preprocess of automatic drawing understanding systems (information statistic, 3D reconstruction).

• Save the storage space.

History• Begin research on vectorization at late 70’s;

• Begin that for engineering drawings at late

80’s;• Related organizations & publications

– IAPR TC-10, IEEE– IEEE T.PAMI, PR, PRL, CVIU, CVGIP, etc.– ICPR, ICDAR, IEEE CVPR, etc.

State of arts

• K. Tombre (LNCS vol.1389, 1998) : “None of these methods works. … Actually, the m

ethods do work, but none of them is perfect.”

• 《 CADALYST 》 performs the annual evaluation on commercial vectorization systems.

Review of existing methods

• Thinning based • CT(Contour Tracking) based• RLE(Run Length Encoding) based• SPT(Sparse Pixel Tracking) based

Thinning-based methods

CT-based methods

RLE-based methods

SPT-based methods

P3

P0

P1

P2P0 P1 P2 P3 P4

Existing difficulties

• Lines with intersections broken into pieces.

• Texts touch lines misrecognition.

• The interference of recognized objects repetitive detection, false detection.

Our research

• Analysis the vectorization model of existing methods.

• Propose an efficient vectorization model for engineering drawings.

• Propose a group of new graphical object recognition algorithms.

Common model of existing methods （ 2PV - 2 Phase Vectorization ）

Raster image

Low level vector form:Short line segments

Graphical objects:Straight lines, circle/arc, curve

Phase 1: Skeletonization or medial-axis approach

Phase 2: Vector-based graphical object recognition

Motivation of 2PV

• Internal memory (RAM)– Used to be high price & limit capacity– High pixel access frequency cause swap

• Pixel tracking algorithm– No guide direction– Repetitive tracking

Object-Oriented Progressive-Simplification based Vectorization Model

• 1 phase model– Imitate the way that humans read drawings

• Recognize a graphical object in its entirety– Object-oriented feature

• Simplify the image data as the recognition goes on– Progressive-simplification feature

Workflow of OOPSV

S-LineRecognition

ArcRecognition

CurveRecognition

SymbolRecognition

TextRecognition

LineSymbolText

ArcCurveSymbolText

CurveSymbolText

SymbolText

Text

S-Line Arc Curve Symbol Text

Graphical object recognition

• Get the intrinsic characteristic of individual type of graphical object.

• Use the characteristic as a guide to track the graphic object in complex environment.

Straight line recognition

Seed segment detection

A

Seed Segment

Irregular run Regular run

Direction guided tracking — based on the Bresenham algorithm

Straight line recognition

O

Vo Vp

Seed segment

Perpendicular runs

Black segment

White segment

Straight line recognition

Dynamic adjustment to tracking direction

PP’

Vp

O

P

P’

Vp

O Pe

Line net recognition

• A line net is a group of intersecting lines.• Take advantage of the intersecting

relationship to accelerate recognition.• Example:

Circle/Arc recognition• Arc segment detection

– get initial arc center, radius, thickness

• Circular tracking– based on the Bresenham algorithm for circle

Circular tracking

P2

P1

O

P

P’

PE

(a) Adjust succeeds (b) Adjust fails

Legend: raster image tracking path medial axis testing path

P2

P1

O P P’

PE

Curve tracking

Tracking result: a sequence of polyline.

P5

P4

P1P2 P3

P8

P9

P7

P6

Pm

P10P11

Image simplification

• Intersection-preserving pixel deletion• Based on the contour detection of the

intersecting branches

Branch at one side

MiddleLine

Branches at both sides

RecognizedLine

Contours of branches

Symbol recognition

• Common symbols– Cartography-based recognition

• Domain-specific symbols– Template-based recognition

Cartography

Symbol template

Text recognition

• Text segmentation– Difficulties: text touches line, similar size

• Character recognition– Stroke-based recognition algorithm

Image before the line recognition

Image after the line recognition

Suspension-Release mechanism

Condition 1: Size( Box(li) ) < Tl Condition 2: p, pl C(p,L) >> C( FP(l,L), L)

n

i=1

Stroke-based character template

• Stroke definition• Black position• White position• Aspect ratio scope• Complexity level

①

②

④

③

⑤

Character recognition

(UMNKLDREFBbhklI1) (BE)

(E) Accept ‘E’, then cut image

Separate connected characters

a. When the rightmost stroke is vertical

b. When the rightmost stroke is not vertical

f(x) x1

•Base on the analysis of the rightmost stroke and the vertical projection.

Experimental result

• Implemented a complete vectorization system running on Windows platform using VC6.0.

• Automatic vectorization of an A0-size drawing (15M) takes about 5 minutes. (PIII500/128M)– Line vectorization takes less than 1 minute (1600 lines),

faster than performing a thinning operation (3.5 mins).

CDI evaluationSize Dp Fp PRI Dv Fv VRI CDI

A0 0.966 0.086 0.940 0.954 0.082 0.936 0.938

A1 0.973 0.077 0.948 0.965 0.084 0.941 0.945

A2 0.975 0.054 0.961 0.963 0.068 0.948 0.955

A3 0.962 0.049 0.957 0.959 0.066 0.947 0.952

This protocol was proposed in Machine Vision Application, (1997)

Manual editing cost evaluationDrawingSize

False_alarms Misses One2many Many2one Total Costs

A0 Ⅰ 124 80 24 37 354Ⅱ 92 62 16 32 263

A1 Ⅰ 63 46 11 18 194Ⅱ 41 33 9 16 142

A2 Ⅰ 30 26 6 8 101Ⅱ 22 18 3 6 67

A3 Ⅰ 16 15 5 7 69Ⅱ 10 13 3 4 44

This protocol was proposed in LNCS V.1389, (1998)

Comparison of editing cost with VPStudio

a. raster image b. our system c. VPStudio

Conclusion: Object-oriented recognition algorithms produce less misrecognition, therefore the editing cost has decreased.

Conclusion

• Progressive simplification decreases both the complexity and workload of vectorization.

• The object-oriented recognition algorithms recognize graphical objects fast and entirely.

Related papers• 7 journal papers & 4 conference papers

• IEEE Trans. PAMI reviewer’s comment: “An efficient model is very important to

recognize engineering drawings …. This paper suggested an object-oriented

progressive-simplification based vectorization system for engineering drawings.

It would bring an impact in this area.”