B9Kg«a«fiaaBa.- aggawwac 
Figure 3: On the left, function / and a set of 2 functions 
h\ and h 2 . On the right, function k computed by toggle 
mapping. 
3 TEXT SEGMENTATION 
Our segmentation step is based on a morphological oper 
ator introduced by Serra (Serra, 1989): Toggle Mapping. 
Toggle mapping is a generic operator which maps a func 
tion on a set of n functions: given a function / (defined 
on Df) and a set of n functions this operator 
defines a new function k by (Fig. 3): 
Figure 4: Result of eq. 4 (function s) on an edge and in 
homogeneous noisy regions. 
Vx G Df k(x) = 
hi(x);Vj G {l.-?7.} 
I f(x) ~ hi(x)\ < I f(x) - hj(xj\l) 
The result depends on the choice of the set of functions hi. 
A classical use of toggle mapping is contrast enhancement: 
this is achieved by applying toggle mapping on an initial 
function / (an image) and a set of 2 functions hi and h 2 
extensive and anti-extensive respectively. 
To segment a gray scale image / by the use of toggle 
mapping, we use a set of 2 functions hi and h 2 with hi 
the morphological erosion of / and h 2 the morphological 
dilatation of /. These two functions are computed by: 
Vx G Df hi(x) 
Vx G Df h 2 (x) 
; min f(y);yev(x) (2) 
max f{y);y e v(x) (3) 
Figure 5: From left to right: 1. Original image, 2. Bina- 
rization (function s from eq. 4), 3. Homogeneity constraint 
(eq. 5), 4. Filling in small homogeneous regions. 
Function s is then improved: 
s{x) = 
0 if\hi(x)-h 2 (x)\<Cmi n 
1 if\hi(x) - h 2 (x)\ >= Cmin 
& \hi(x) - f{x)I <p*\h 2 (x) - f(x)I 
2 otherwise 
with v(x) a small neighborhood (the structuring element) 
of pixel x. Then, instead of taking the result of toggle 
mapping k (eq. 1), we keep the number of the function on 
which we map the pixel. This leads us to define function 
s: 
Vx G Dfs(x) = i;\fj G {1..2}\f(x)-hi(x)\ < \f(x)-hj(x)\ 
(4) 
Function s(x) takes two values and may be seen as a bi- 
narization of image / with a local criterion (Fig. 4 left). 
Our function efficiently detects boundaries but may gener 
ate salt and pepper noise in homogeneous regions (Fig. 4 
right): even very small local variations generate an edge. 
To avoid this, we introduce a minimal contrast c min and if 
\hi(x) — h 2 (x)\ < Cmin, we do not analyse the pixel x. 
(5) 
Then, no boundary will be extracted within homogeneous 
areas, s is a segmentation of / (notice that now we have 3 
possible values instead of 2: a low value, a high value and 
a value that represents homogeneous regions). 
To use this method efficiently, some parameters must be 
set up: the size of the structuring element used to com 
pute a morphological erosion (hi) and a dilation (h 2 ), the 
minimal contrast Cmi n and an additional parameter p. Vari 
ations of p influence the thickness of detected structures. 
Getting three values in output instead of two can be dis 
turbing. Many strategies can be applied to assign a value 
to homogeneous regions (to determine whether the region 
belongs to low value areas or high value ones): if a region 
is completely surrounded by pixels of the same value, the 
whole region is assigned to this value. Another strategy 
consists in dilating all boundaries onto homogeneous re 
gions. In our case, this is not a real issue: as characters 
are narrow, it is not common to have homogeneous regions 
inside characters and if it occurs, such regions are small. 
Then, our strategy consists in studying boundaries of small 
homogeneous regions in order to fill a possible hole in 
characters. Bigger homogeneous regions are mostly left 
unchanged, only a small dilation of these boundaries is per 
formed. 
Illustration of the segmentation process is given in Fig 
ure 5. In the rest of the paper, this method is called Toggle 
Mapping Morphological Segmentation (TMMS).