Optical Character Recognition Based on Least Square Support Vector Machine

Optical character recognition (OCR) is a very active field for research and development, and has become one of the most successful applications of automatic pattern recognition. To avoid the curse of dimensionality and improve the recognition performance, an optical character recognition system based on image preprocessing technologies combined with least square support vector machine (LS-SVM) has been developed, which first uses dynamic thresholding operation and robust gray value normalization to segment characters and extract features respectively, and then uses LS-SVM to classify characters based on features. The proposed method has been evaluated by carrying out recognition experiments on the optical characters of electronic components. The results show that the proposed method has a better recognition performance, and holds a lot of potential for developing robust recognition learning.