**1. Introduction**

The potential applications of Wavelet Transform (WT) are limitless including image processing, audio compression and communication systems. In image processing, WT is used in applications such as image compression, denoising, speckle removal, feature analysis, edge detection and object detection. The use of WT algorithms in image processing for real-time custom applications may require dedicated processors such as Digital Signal Processor (DSPs), Field Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs) as reported in (Ma et al., 2000), (Benkrid et al., 2001) and (Wong et al., 2007) respectively.

The interest in this chapter is the use of WT in image objects segmentation, in particular, in the area of Automatic Number Plate Recognition (ANPR) also known as License Plate Recognition (LPR). ANPR algorithm is normally divided into three sections namely LP candidate detection, character segmentation and recognition. The focus of this chapter is on the use of Haar WT algorithms for License Plate (LP) character segmentation on a DSP using Standard Definition (SD) and High Definition (HD) images. This is an extension of the work reported in (Musoromy et al., 2010) by the authors, where Daubechies and Haar WT are used to detect image edges and to enhance features of an image to detect a LP region that contain characters. The work in (Musoromy et al., 2010) demonstrated that 2D Haar WT is favourable in ANPR using DSP due to its ability to operate in real-time. The drive here is the consumer interest in real-time standalone embedded ANPR systems. The next section describes the proposed LP character segmentation algorithm.

The chapter organisation is as follows: Section (2) reviews dedicated hardware for WTbased image processing algorithms. Section (3) gives a review of image processing techniques using WT and in ANPR application. Section (4) presents the proposed LP character segmentation algorithm based on 2D Haar WT edge detector. Section (5) presents experimental setup. Section (6) presents results and analysis. Section (7) gives conclusion and Section (8) gives references.

Real-Time DSP-Based License Plate

**3. Image processing and ANPR using WT** 

cameras in close proximity to the plate (Wu et al., 2009).

plate image can be applied (Paunwala et al., 2010).

this work is summarized in the following section.

**3.1 LP detection algorithm** 

Character Segmentation Algorithm Using 2D Haar Wavelet Transform 5

This section gives a review of interesting ANPR algorithms using WT. The use of discrete wavelet transform (DWT) (described in Section 4.2) in ANPR is reported by Wu (Wu et al., 2009) in LP detection process. The methodology works by applying the **"high-low"** subband feature of 2D Haar DWT twice to increase the recognition of vertical edges while decreasing background noise in real world applications. The authors noted an increase in the ease of location and extraction of the license plate by orthogonal projection histogram analysis from the scene image in comparison with the vertical Sobel operator (a single level 2D Haar DWT) used in most License Plate Detection Algorithms. However, due to the downsampling used in this technique, it is only suitable for use with high-resolution images or

An interesting algorithm is proposed by Roomi (Roomi et al., 20011) that consists of two main modules, one for the rough detection of the region of interest (ROI) using vertical gradients and another for the accurate localisation of vertical edges using the vertical subband feature of 2D discrete wavelet transform (DWT). This is followed by the identification of the orthogonal projection histogram for the extraction of the license plate. This method combines the advantage of relatively short runtimes whilst still maintaining accuracy, across a range of vehicle types. The authors reported that the number plates

WT is also used in the simplification of skew correction in order to reduce computational demands to make the process suitable for real time applications (Paunwala et al., 2010). The method uses two levels WT to extract a skewed feature image of the original LP image, which is then transformed into a binary image from which the feature points can be identified by applying a threshold. These feature points help identify the angle at which the plate is tilted using principal component analysis, from which the correction to the whole

To conclude, the use of WT and the advantages are widely reported in the ANPR algorithms and therefore the focus of this chapter is the suitability of WT in HD images and DSPs for real time performance in LP character segmentation but firstly, LP detection process used in

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

recognition accuracy was reduced where the plates were tilted (Roomi et al., 20011).
