Paper Title : Smart Security Based On Automatic Number Plate Recoginition
ISSN : 2394-2231
Year of Publication : 2020
MLA Style: Mr.R. Navin Kumar, M C A, M Phil, Mr. S. K. Sedhu Raman, M C A, "Smart Security Based On Automatic Number Plate Recoginition" Volume 7 - Issue 2 March - April,2020 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org
APA Style: Mr.R. Navin Kumar, M C A, M Phil, Mr. S. K. Sedhu Raman, M C A, "Smart Security Based On Automatic Number Plate Recoginition" Volume 7 - Issue 2 March - April,2020 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org
This venture is entitled as “SMART SECURITYBASED ON AUTOMATIC NUMBER PLATE RECOGNITION”, is developed via the usage of MATLAB as the front end. This automation can predIJCT the quantity plate from an enter photo and compares the anticipated number plate with registered automobiles quantity plate then it's going to displays the end result to the user. Automatic Number Plate Recognition is an pIJCTure processing technology and important subject of studies that identifies automobiles via their wide variety plates in the course of which the amount plate facts is extracted from vehicle photo or from series of photographs without direct human intervention. ANPR consist of four phases: Preprocessing, number plate extraction, character segmentation, characters recognition. This paper gives an efficient approach for number plate extraction from pre-processed vehicle's enter photograph using morphological operations, edge detection and connected factor the enter image is first pre-process the usage of iterative bilateral clear out and adaptive histogram equalization. The important goal of this assignment is to examine the predicated range plate from an input pIJCTure to registered vehicle photos. If the range plate from input photo fits the registered vehicle’s wide variety plate it suggests the precise end result for user. Here the guide attempt for comparing variety plates with registered number plate has ignored.
 Rakesh Agrawal Jerry Kiernan et al , “Skeleton pruning by contour partitioning with discrete curve evolution,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 3, pp. 449–462, Mar. 2004.  Claudio Lucchese et al, “A weighted finite-state framework for correcting errors in natural scene OCR,” in Proc. 9th Int. Conf. Document Anal. Recognit., Sep. 2001, pp. 889–893.  Vıctor R. Doncel, Nikos et al, “Automatic detection and recogniton of signs from natural scenes,” IEEE Trans. Image Process., vol. 13, no. 1, pp. 87–99, Jan. 2000.  A. Coates et al., “Text detection and character recognition in scene images with unsupervised feature learning,” in Proc. ICDAR, Sep. 2011, pp. 440–445.  N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2005, pp. 886–893.  T. de Campos, B. Babu, and M. Varma, “Character recognition in natural images,” in Proc. VISAPP, 2009.  B. Epshtein, E. Ofek, and Y. Wexler, “Detecting text in natural scenes with stroke width transform,” in Proc. CVPR, Jun. 2010, pp. 2963–2970.  P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, “Object detection with discriminatively trained part-based models,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 9, pp. 1627–1645, Sep. 2010.  Rakesh Agrawal and Jerry Kiernan et al , “Learning shape prior models for object matching,” in Proc. CVPR, Jun. 2006, pp. 848–855.  N. F. Johnson, Z. Duric, “Text extraction and document image segmentation using matched wavelets and MRF model,” IEEE Trans. Image Process., vol. 16, no. 8, pp. 2117–2128, Aug. 2000.
ANPR, GIS, GPS, NP, NN.