KarNuPeD car number plate detection and recognition - Bautista, Sherry Anne To; Guiriba, Michelle Macam; Lagman, Aimee Dungca; Mahinay, Josephine Joy Ang. (1999)
Vehicle identification system can be used in applications such as unattended parking systems and can be extended to automatic traffic law enforcement. One way to identify a vehicle is to identify its plate number. Car number identification system consists of two parts: detection of the location of the plate in the digitized image and recognition of the plate number. KarNuPeD uses four modules to fulfill these two parts: color recognition and classification, plate detection, character segmentation, and character recognition. Color recognition and classification uses SOM and is used to standardize the input digitized image. The standardized image is just the digitized image with colors classfied into six - green, yellow, black, white, red, and cyan to represent all the other colors. The standardized image is divided into grids and counts the frequency of the colors present in each of the grids. If a grid has winners (the top two most frequent colors present) that correspond to a color combination that of plate number (i.e. green-white. red-white. yellow-black). It is noted as a candidate plate number grid. Then a tally of the candidate grids is made. The tally is to determine which color combination appears in most of the candidate grids, then if a cluster of candidate grids with the considered color combination is found to with width 31% of that of the length, a plate region is detected. The detected plate is first binarized before it is inputted for character segmentation. The binarized image of the plate is the detected plate, which is reduced to having two colors only (the considered plate color combination). The detected plate is binarized to simplify character segmentation. Segmentation is done by individually enclosing each plane character. The segmented characters are then passed to the next module for character recognition. SOM performance is 53.5% for green-white plates, 90% for yellow-black plates, and 40% for red-white plates. Plates detection with correct foreground and background determination is 57.7% for green-white, 45% for yellow-black, and none for red-white. Recognition rate is 24%. Plate detection and segmentation is 60% for green-white, 100% for yellow-black, and none for red white. These results are highly dependent on the gathering of the inputs for the training data set and the training of the neural networks (SOM, MLP) used.
Thesis (B.S. Computer Science)--University of the Philippines, Diliman.
Keywords: Pattern perception; Image reconstruction; Optical pattern recognition