Automatic segmentation and tilt correction of music images are key techniques in music recognition. Various computer optical music score recognition technologies have been widely used in the digitization of music score images

but there has been little research on the recognition of numbered musical notation. In this paper

an automatic image segmentation algorithm based on PCNN (Pulse Coupled Neural Networks) and DNN (Deep Neural Networks) is proposed to solve the problem of a variety of lighting conditions. The image is processed by adaptive block processing according to the gray scale distribution of the spectral image and analyze the relationship between the gray scale characteristics of each block and the optimal PCNN iteration time

construct an appropriate DNN neural network to realize the adaptive selection of optimal segmentation graph. Further using the horizontal projection of bar lines

we propose a dual-scale descent method to realize the skew correction of numbered musical notation image. We propose an edgeless vertical projection method and connected domain distance judgment method to extract the note and lyrics from numbered musical notation image. Simulation experiments show that the proposed algorithm exibits better robustness for numbered musical notation image under complex illumination conditions and faster speed.