**3.1 LP detection algorithm**

The LP detection is the first part of an ANPR algorithm, which gives the rectangle region that contains characters. The plate detection algorithm used here is divided into four parts. These are input image normalization, edges enhancement using filters, edges finding and linking to rectangles using connected component analysis (CCA) and plate candidate finding (Musoromy et al., 2010). We have used the edge finding method in (Musoromy et al., 2010) to verify the presence of an edge. The edge finding method works by scanning the image and a list of edges is found using contrast comparison between pixel intensities on the edges' boundaries using the original gray scale image. The WT methodologies described by the authors in the literature above are mainly applied to LP detection process and benchmarked on baseline processors. In this chapter, we have expanded the use of Haar based edges in LP character segmentation algorithm. In addition, we have applied these

Real-Time DSP-Based License Plate

Character Segmentation Algorithm Using 2D Haar Wavelet Transform 7

The x and y directions can represent rows and columns of an image f(x,y) Є L2(R2) and therefore we can also apply the CWT in 2D using wavelet Ψ Є L2(R2) as (Palacios et al., 2011):

1 xu y v W f u,v f(x,y)<sup>Ψ</sup> , s ss

1 x <sup>y</sup> <sup>Ψ</sup> x,y <sup>Ψ</sup> , s ss

The large number of coefficients produced by CWT makes it necessary to discretely sample signals in order to simplify signal analysis process and also for the use in real-time applications such as image processing. This process is technically known as discrete wavelet

Discrete wavelet transform (DWT) or fast wavelet transform (FWT) is a specialised case of sub-band filtering, where DWT is a sampled signal of size N using scale at 2*<sup>j</sup> s* for j < 0

<sup>1</sup> <sup>Ψ</sup> [ ] <sup>Ψ</sup> *<sup>j</sup>*

*n*

Wf n,s f m Ψ m n f\*Ψ [n]

Calculations of DWT is done using filter bank which can be a series of cascading digital filter. Implementing the DWT using filter banks entails the signal sampled being passed through high-pass and low-pass filters simultaneously to produce detailed and approximated confidents respectively (Qureshi, 2005). The high frequencies DWT are

W f n,s f m Ψ m n f\*Ψ [n]

m 0

High j m 0

*n*

*s s*

N 1 \* <sup>Θ</sup> j

N 1 \* <sup>Θ</sup>

and time (for scale 1) (Mallat, 1999). Using the wavelet equation:

The convolution of signal f and the wavelet is written as follows:

(4)

<sup>Θ</sup> W f u,v <sup>s</sup> <sup>s</sup> f\*<sup>Ψ</sup> u,v (6)

(5)

(7)

<sup>Θ</sup> \* Ψ [n] Ψ [n] *<sup>j</sup>* (8)

(9)

(10)

\*

<sup>s</sup>

 

 

s

We can rewrite equation (4) with dilation factor s as

and <sup>Θ</sup> <sup>Θ</sup> Ψ (x,y) Ψ ( x, y) as a convolution

transform (DWT).

**4.2 Discrete Wavelet Transform** 

DWT is also a circular convolution where:

contained similar to equation (9) as follows:

edges in HD images and benchmarked their DSP and baseline processor performance to meet real-time requirement.
