摘要：Due to the low recognition accuracy, the remote meter reading technology based on camera direct reading has been developed slowly. Although there is a variety of features data for recognizing digit in image using BP neural network, some of data cannot be used to recognize digit accurately. Moreover, the BP network has a slow rate of convergence, low accuracy and easily fall into local minimum. To solve the above questions, a new digit recognition algorithm of meter based on rough set and neural network which are optimized by genetic algorithm is proposed. The improved genetic rough set algorithm is used for reducing the data, and then the minimum feature attribute sets after reduction are input to genetic neural network for identifying digit. The experimental results show that the algorithm can effectively reduce the number of decision attributes and simplify the structure of the neural network with high identification accuracy and short training time, which improve the generalization ability and robustness of the neural network.
