7A-5-7 
(a) (b) 
Figure 13: (a) A pentagon imager(b) 10 magnitudes 
of NFD of a pentagon 
6 Text region detection 
Traffic signs may have characters which indicate traf 
fic meanings. UsuallyFsuch text traffic signs have an 
uniform background color and an uniform foreground 
color for text. Thus the texts have high contrast with 
the background. A definition of texture energy can 
be used for describing such contrast measurements. If 
such text image is viewed in 3D spaceTthis measure 
ment describe relative smoothness or roughness of the 
flat plane. 
Texture energy of a pixel can be defined on a local 
window W centered at this pixel which is also call spa 
tial variance widely applied in document image anal 
ysis to separate text regions and image regions: 
E =jrrj E (I(iJ)-i(iJ)) 2 (3) 
(ij)ew' 
where 
= j E w.i» 
and P is the number of pixels in the window W 
Textness can be define by 
textness =1 —— 
1 + E 
For a constant intensity window which all pixels 
have the same valueTtexteness = OTand approaches 
1 for large values of E (high contrast). 
Text region detection. Texts in a traffic sign are 
almost horizontal. A horizontal window (w x 1) can 
be selected to compute the texture energy. 
Sliding such window over the whole imageT a 
textness image is obtainedT a local thresholding 
method is used to segment this textness image. Be 
cause text lines are horizontalThorizontal line-shaped 
blobs with a certain size of area above are kept to be 
masks to determine the text regions. To do soTtwo 
measurement are computedrnamely eccentricity and 
orientation. 
The eccentricity is 
_ (m 2 o - m 02 ) 2 + 4 
(m 20 + m 02 ) 2 
НегеГm pq is the moment of (pEi) orders. It ranges 
from 0 to lTzero for a circular objectrand one for a 
line-shaped object. 
The orientation is 
Ф — i arctan2(2mn,m 20 - m 0 2) 
The horizontal line-shaped object will has an ap 
proximate orientation of zero or ±n. 
Aided by vertical edges. Directly usage of equa 
tion (3) to compute textness will lead to too many 
false alerts for text containing regions. For exampler 
on borders of objects in an imageFonly one edge exists 
in a local window may with high contrastrthe textness 
of the pixel will be near to one. To disambiguate this 
where the number of vertical edges in the local window 
is too smallTthe texture energy is set to zero. Results 
are shown in Figure 14 and Figure 15. 
Connected component analysis. The above al 
gorithm may not work on images taken in street or 
in winterFwhich may contain frames of wall windows 
or branches of trees. As we knowT characters in a 
text lines are horizontally aligned and each line is sep 
arated vertically. A further text extraction can be 
done on the detected text regions based on connected 
component analysis. Performed aft-егГа thresholding 
method is used for further segmentation followed by 
a connected component analysis. A minimal bound 
ing rectangle which encloses each connected compo 
nent is found. For a true text liner these rectangles 
must approximately have the same size and align on 
a horizontal line. This result can be used not only for 
verification of text regionsTbut also in a traffic sign 
recognition system. Results are shown in Figure 16. 
Adaptive determination of local window size. 
One problem with the above approach is determina 
tion of the size of the local window. Small local win 
dows cannot result in detection of text regions with 
large fonts. To solve this ргоЫетГап adaptive text 
location method is under development. One possible 
method is to adaptively increase the size of the local