The Model Of Numerals Recognition Based On PCNN And FPF

Numerals Recognition has a prosperous future in the field of simulating artificial intelligence and computer words processing.Researchers worldwide worked on pattern recognition and proposed many algorithms of image preprocessing and pattern recognition.But none can compare with the recognition ability of human beings.This inspires researchers to improve the image pre-processing algorithm,feature extraction algorithm and recognition algorithm.The image recognition for mankind is mainly dependent on the feature extraction of information.The main problem in target recognition is that images of targets will change with translation,rotation scale and intensity,etc and feature extraction of information is one of the most important steps to solve.But relatively few researches has been done that uses PCNN in numerals recognition.The main problem in target recognition is that images of targets will change with translation,rotation scale and intensity.A numerals recognition model based on PCNN(Pulse-Coupled Neural Network) and FPF (Fractional-Power Filter) is proposed.This method uses inherent ability of PCNN to extract feature and capability of FPF,then FPF allows invariance to be built into and it can recognize numerals with distortion effectively.The results of computer simulation show that the proposed method has a better effect compared with classical filters such as MACE. The simulation results of 340 images of the numerals from 0 to 9 with translation,rotation and scale demonstrate that the method works well and gets high distinguishing rate.