The Application Of Pattern Recognition On Water Meter

Water meter automatic recognition system is an important application of pattern recognition and computer vision in our daily life;and the image processing and digits recognition are its two core technologies.In this paper,we will introduce a way to build a recognition system based on the features of the true color images,including image pre-processing,feature extraction and classifying.The image pre-processing mainly based on the HSV color space.The useful information of the three parameters(H,S,V) and the significant algorithm form a effective image pre-processing.We should first convert the format of the color images from RGB to HSV.Then we can detect the parallel through the algorithm of Canny and Hough to obtain the obliquity of the image to correct it,and this is based on the parameter of V.Also,we use the parallel to locate the digits region.In binarization step,the H and S parameter are useful:we propose the S parameter and H parameter features fusion ruler and the C-means and H parameter features fusion ruler to distinguish the digits from background.The two means are effective and both have its merit and defect,and the C-means and H parameter threshold fusion ruler is the final choise.Two major units compose the segment of digits:the pixels and the figure of the digits.Finally,we resize all the pictures into the same size.We choose the statistical features as the extraction features of digits.Four lines(including two horizontal and two vertical lines) divide the digit into nine regions.We total the number of the black dots to divide the total dots in each region as the features of the digits.Finally,a three-layer BP neural network is applied in this system to identify the number 0 to 9;the number of the neurons in the middle-layer of BP network is defined by the experiment result on matlab.We test the system by hundreds of water meter images available,and improve the system according to the experimental results.The results show the final proposed scheme is feasible and has great robustness.