Table 1.

Previous Reviews from Literature.

Year	From	Techniques Reviewed
2018	[16]	Reviewed Myanmar Researched Papers Only.
		Khin et al. in the year 2018 -Threshold based approach and bounding box -All colors, and conditions images are used
		Htay et al. in the year 2016 -Neural Network Method for Plate Localization -Noisy, tilted plates or other degraded forms of number plate were not considered em -Very limited number of samples tested
		Sinha et al. in the year 2011 -Character extraction and recognition -Pattern recognition based on fuzzy logic -Did not yield good results for skewed plate
		Bai et al. in the year 2004 -Hybrid Approach -Edge detection and morphological operations -Tilted/skewed characters are not tested
ine 2017	[17]	This research reviewed various techniques for each stage of ANPR system.
		License Plate Extraction: -Edge information Analysis -Probabilistic model -Subspace Projection and Probabilistic Neural Network -Blob Analysis, Mathematical Morphology -Color Space and Geometrical Properties -Thresholding, Histogram, Computational Intelligence and Adaptive Boost techniques
		Segmentation: -Gabor transform -K-Means Algorithm -Tree of Shapes -Hidden Markov Chains
		Recognition: -Optical Character Recognition (OCR) -Embedded DSP-Platform -Pattern match method -Computational Intelligence - Neural Networks
2016	[18]	Reviewed techniques:
		Feature salience, Hough Transform, Neural Networks, Histogram techniques, Chin code, Tophat Filtering, Template matching, Matrix mapping, wavelet transform, SVM, Pulse Coupled Neural X Networks, SIFT feature points, Gaussian Filtering and other modified NNs.
2015	[19]	Techniques reviewed for each stage:
		Number Plate Extraction -Method 1 *Sobel based Vertical Edge Detector *Sliding window technique, it has the size similar to that of the number plate size *Requires prior knowledge of the number plate size *Threshold tuning required and needs to setup for each set -Method 2 *Gradient analysis *Vertical histogram along with morphology techniques *Connected Component Analysis -Method 3 *Geometrical shape of the number plate
		Character Segmentation Method -Pixel projection in which both vertical and horizontal directions employed -Fast and robust -Dealt with tilt factor by adding additional layer of vertical projection
		Character Recognition Techniques -Method 1 *Template Matching *Adaptive threshold *Pixel-wise matching is performed *The simplest method known for recognition -Method 2 *Neural Networks classifiers
		The performance of these techniques reviewed is summarized in Table 2.