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This paper brings out an algorithm to recognize the numerical characters for a given kind of water meter. Because of the same scales of the width and height of the target area, this algorithm makes a feature pattern which has three gray scales according to the target area and zoom the feature pattern to fit for segmenting. These three gray scales divide the whole 256 gray scales of our image into 7 types and the authors define the different weights for these 7 kinds of gray scales when making feature pattern matching. The characters rolls in water meter and they can be displayed in two modes, one is whole displayed and the other is half displayed(this mode means that there are two half characters displayed in the water meter at the same time). It uses an adaptive learning BP neural networks to train the samples of 10 whole characters and 10 "two half characters", the software recognize the character based on the trained BP neural network. The results show that full using of the features of the target area can improve the accuracy of character segmentation and the classical recognition algorithm can obtain a good effect base on these methods.