Real-time reading recognition of digital display instrument based on BP neural network

In many chemical industries, the metallurgy, a number of digital real-time monitoring instruments are used. The manual method will bring the problems of inefficient and misjudge. To recognize digital display instrument´s real-time reading, a BP neural network is designed, an improved BP algorithm and fifteen feature extraction method is proposed. The image of instrument board is obtained by an digital camera firstly, then transmitted to PC, and an image preprocessing is carried through. The image preprocessing includes vertical tilt correction, binary, gray processing, grads sharp, noise point removal, character segmentation, character unitary adjustment, character contraction and feature extraction. Character features are extracted by n dimensions feature extraction method. Finally real-time recognize the reading by the BP neural network. The experiment results show that the recognition accurate rate is greater than 98%. And the time of recognizing for one instrument image is less than 0.5 second.