000			03142nam a2200265 a 4500
001			vtls000058750
003			KUKTEM
005			20251114204513.0
008			120321t2011 my a f m 000 0 eng d
020			_aTHE0002939(Local)
039		9	_a201905140825 _bzul _c201203211116 _dida _y201203211004 _zida
040			_aUMP
090			_aTA1637 .A45 2011 rs Thesis
100	0		_aNor Amizam Jusoh
245	1	0	_aSegmentation and recognition of Malaysian car plates using Freeman chain codes / _cNor Amizam Jusoh
260			_aKuantan, Pahang : _bUMP, _c2011
300			_axiv, 105 p. : _bill. (some col.) ; _c30 cm. + _e1 CD-ROM
502			_aThesis (Master of Science (Computer)) -- Universiti Malaysia Pahang - 2011
504			_aBibliography : p. 90-96
520	3		_aResearch on automatic car plate recognition has been widely and intensively conducted all over the world with the application of various types of segmentation and recognition techniques by researchers. Most of the recognition techniques applied are not focusing on shape-based recognition although through normal human vision, each character in the car plate has a unique and different shape with each other. The chosen techniques have contributed into many proposed suitable methodologies for car plate recognition research but with the same objectives; to gain high or increase the segmentation and recognition accuracy rate with less processing time. This research is conducted with the aim of identifying the suitable or appropriate segmentation technique which can be used to segment either the standard or non-standard specification car plates. Besides that, the objective is also to study whether the shape-based recognition technique is efficient and accurate enough to recognize Malaysian car plates which are varied in terms of font types. Techniques that have been chosen for segmentation process are pixel count, connected component labeling (CCL) and a proposed technique; the connected component labeling with minimum object removal. As for recognition purpose, the techniques that have been experimented are the Freeman chain codes (FCC), template matching and a proposed technique; the Freeman chain codes with characters’ features (FCCwF). The results from the experiment shows that the proposed segmentation technique; the connected component labeling with minimum object removal able to increase the segmentation success rate by more than 96% and the proposed recognition technique; the Freeman chain codes with characters’ features is able to reach the accuracy rate of 95% compared to other tested techniques. Based on the experiments and results, Freeman chain codes are efficient and accurate enough to recognize various font types of Malaysian car plates for most of the characters with less processing time of 0.1s but a higher recognition accuracy rate can be gained by combining FCC with characters’ features.
650		0	_aImage processing _xDigital techniques
650		0	_aOptical pattern recognition
650		0	_a Computer vision
999			_aVIRTUA40 _c3164 _d3170
999			_aVTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5020*5040*5200*6500*6501*6502*9992