The efficiency of an automatic number plate recognition system depends directly on the proper effective preprocessing of the number plate. The OCRs available for recognition are capable of reading the number plates which are in proper orientation of 0 0. In many situations the vehicle number plates captured may be in any different orientation like 90 0 , 180 0 and 270 0. These orientations in number plates are due to declamping of number plate at one end or toppling of vehicle. Such differently oriented number plates cannot be subjected for reading by OCRs and such situations require the system to detect the direction of orientation and correct the same before subjecting the same for reading. The efficiency of an automatic vehicle number plate recognition is high if the necessary preprocessing methods give results effectively. Also an essential and important preprocessing in automatic number plate recognition system is correction of skewed number plates in vehicle images which is mainly due to position of camera while capturing the vehicle image. The skewed number plate affects badly on the accurate character segmentation and recognition. Once the number plate is segmented from the vehicle image, the plate has to be checked for skewness and the same has to be corrected for future processing. This paper proposes a work to detect the orientation and skewed of segmented number plates from vehicle image using hybrid approach combination of Autocorrelation and Radon transform. A good volume of training samples are generated synthetically to train the system and the system is tested using sufficient test samples. The results of system shows an overall efficiency 65.05% of oriented detection, efficiency of 85.71% skewed number plate and performs an essential preprocessing in an automatic number plate recognition system.
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