Rejection threshold estimation for an unknown language model in an OCR task
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
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This paper presents a Linear Discriminant Analysis based Measurement (LDAM) on the output from classifiers as a criterion to reject the patterns which cannot be classified with high reliability. This is important in applications (such as in processing of financial documents) where errors can be very costly and therefore less tolerable than rejections. To implement the rejection, which can be considered to be a two-class problem of accepting the classification result or otherwise, Linear Discriminant Analysis (LDA) is used to determine the rejection threshold at a new approach. LDAM is designed to take into consideration the confidence values of the classifier outputs & the relations between them, and it is an improvement over traditional rejection measurements such as First Rank Measurement (FRM) and First Two Ranks Measurement (FTRM). Experiments are conducted on the CENPARMI Arabic Isolated Numerals Database. The results show that LDAM is more effective, and it can achieve a higher reliability while achieving a high recognition rate.