Abstract

Since the early 1990s, many research communities, amongst the pattern recognition and machine learning, have shown a growing interest in Multiple Classifier Systems (MCSs), particularly for the recognition of handwritten words and numerals. This thesis is divided into two parts. First, we construct an effective hybrid MCS (HMCS) of handwritten numeral recognition in order to raise the reliability of the entire system. This HMCS is proposed by integrating the cooperation (serial topology) and combination (parallel topology) of three classifiers: SVM, MQDF, and LeNet-5. In cooperation, patterns rejected from the previous classifier become the input of the next classifier. Based on the principles of different classifiers, effective measurements for the rejection options---First Rank Measurement (FRM), Differential Measurement (DM), and Probability Measurement (PM) are defined. In combination, Weighted Borda Count (WBC) at the rank level, which reflects confidence and preference of different ranks in different classes with different classifiers, is applied. Second, we analyze factors that cause the errors in HMCS. In this process, we focus mainly on the role of size normalization on the recognition of handwritten numerals.