OPTIMIZATION OF REJECTION PARAMETERS TO ENHANCE RELIABILITY IN HANDWRITING RECOGNITION

In pattern recognition, optimizing the parameters in rejection can achieve a high reliability while maintaining a high recognition rate. When rejection is considered to be a two-class problem of accepting the classification result or not, Linear Discriminant Analysis (LDA), which is an effective classification method, can be used to optimize the criterion for rejection. In this work, a Linear Discriminant Analysis Measurement (LDAM) is designed to take into consideration the confidence values of the classifier outputs and the relations between them. In the process, the First Rank Measurement (FRM), First Two Ranks Measurement (FTRM), and other traditional rejection measurements are reviewed. The use of FRM is an absolute rejection strategy that considers only the top choice for rejection, while using FTRM is a relative rejection strategy that considers the top two choices. Experiments are conducted on the CENPARMI Arabic Isolated Numerals Database with outputs that can represent distances or probabilities from different classifiers such as HeroSVM and LibSVM. The results show that the use of LDAM is more optimal than FRM and FTRM in producing reliable recognition results.