CHAPTER 2
LITERATURE SURVEY AND PROJECT BACKGROUND
This chapter discuss the summary of previous work related to the project, the theory of OCR technique and proposed model of the vision system design.
2.1 Journal Literature
The comparison between all the previous works related to project such as Optical Character Recognition for Automatic License Plate Recognition, Optical Character Recognition
on document reading, and Optical Character Recognition on surface product item. All this previous work are very helpful by giving a lot of ideas and knowledge which can implement
and apply to the project.
2.1.1 Automatic License Plate Recognition
In 2003, Muhammad Sarfraz, Mohammed Jameel Ahmed, and Syed A. Ghazi et al.[1] presented the license plate recognition methods as shown in Table 2.1. Recognition rate proved
to be 96.22 for the extraction of plate region, 94.04 for the segmentation of the characters and 95.24 for the recognition unit accurate, giving the overall system performance 95
recognition rate. This approach having some problem in extracting the plate, diplomatic cars and military vehicles, are not addressed since they are rarely seen. Detection only for white,
black, red, and green color plate or numbers.
Table 2.1: The OCR techniques used by Muhammad Sarfraz, Mohammed Jameel Ahmed, and Syed A. Ghazi to recognize the license plate.
In 2005, Serkan Ozbay, and Ergun Ercelebi et al.[2] recognized OCR techniques as shown in Table 2.2. Final output it is proved to be 97.6 for the extraction of plate region, 96
for the segmentation of the characters and 98.8 for the recognition unit accurate, giving the overall system performance 92.57 recognition rate. This system is designed for the
identification of Turkish license plates only.
Table 2.2: The OCR techniques used by Serkan Ozbay, and Ergun Ercelebi to recognize the license plate.
Image Acquisition
Pre- processing
Segmentation Feature Extraction
Recognition Post-
processing
Digital camera
Vertical edge detection
by sobel
algorithm, Filtering by
seed filling algorithm,
Vertical Edge Matching
Normalization, Template
matching
Image Acquisition
Pre- processing
Segmentation Feature
Extraction Recognition
Post- processing
Smearing algorithms,
Filtering, Morphological
algorithms Edge detection
algorithms, Smearing
algorithms Template
matching
In 2010, Kumar Parasuraman and P.Vasantha Kumar et al. [3] proposed an algorithm consists of three major parts as shown in Table 2.3. The overall system performance 98
recognition rate. The proposed method is mainly designed for real-time Malaysian license plate, and can be readily extended to cope with license plates of other countries, especially those using
Latin characters.
Table 2.3: The OCR techniques used by Kumar Parasuraman and P.Vasantha Kumar to recognize the license plate.
2.1.2 Optical Character Recognition on document reading
