caa a22 4500 445379553 CHVBK 20180317143013.0 cr unu---uuuuu 170323e20110901xx s 000 0 eng 10.1007/s10032-011-0154-8 doi (NATIONALLICENCE)springer-10.1007/s10032-011-0154-8 Rejection measurement based on linear discriminant analysis for document recognition [Elektronische Daten] [Chun He, Louisa Lam, Ching Suen] In document recognition, it is often important to obtain high accuracy or reliability and to reject patterns that cannot be classified with high confidence. This is the case for applications such as the processing of financial documents in which errors can be very costly and therefore far less tolerable than rejections. This paper presents a new approach based on Linear Discriminant Analysis (LDA) to reject less reliable classifier outputs. To implement the rejection, which can be considered a two-class problem of accepting the classification result or otherwise, an LDA-based measurement is used to determine a new rejection threshold. This measurement (LDAM) is designed to take into consideration the confidence values of the classifier outputs and the relations between them, and it represents a more comprehensive measurement than traditional rejection measurements such as First Rank Measurement and First Two Ranks Measurement. Experiments are conducted on the CENPARMI database of numerals, the CENPARMI Arabic Isolated Numerals Database, and the numerals in the NIST Special Database 19. The results show that LDAM is more effective, and it can achieve a higher reliability while maintaining a high recognition rate on these databases of very different origins and sizes. Springer-Verlag, 2011 Document recognition nationallicence Linear discriminant analysis nationallicence Rejection criterion nationallicence Recognition reliability nationallicence He Chun Department of Computer Science and Software Engineering, CENPARMI (Centre for Pattern Recognition and Machine Intelligence), Concordia University, H3G 1M8, Montreal, QC, Canada aut Lam Louisa Department of Computer Science and Software Engineering, CENPARMI (Centre for Pattern Recognition and Machine Intelligence), Concordia University, H3G 1M8, Montreal, QC, Canada aut Suen Ching Department of Computer Science and Software Engineering, CENPARMI (Centre for Pattern Recognition and Machine Intelligence), Concordia University, H3G 1M8, Montreal, QC, Canada aut International Journal on Document Analysis and Recognition (IJDAR) Springer-Verlag 14/3(2011-09-01), 263-272 1433-2833 14:3<263 2011 14 10032 https://doi.org/10.1007/s10032-011-0154-8 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10032-011-0154-8 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- He Chun Department of Computer Science and Software Engineering, CENPARMI (Centre for Pattern Recognition and Machine Intelligence), Concordia University, H3G 1M8, Montreal, QC, Canada aut NATIONALLICENCE 700 1- Lam Louisa Department of Computer Science and Software Engineering, CENPARMI (Centre for Pattern Recognition and Machine Intelligence), Concordia University, H3G 1M8, Montreal, QC, Canada aut NATIONALLICENCE 700 1- Suen Ching Department of Computer Science and Software Engineering, CENPARMI (Centre for Pattern Recognition and Machine Intelligence), Concordia University, H3G 1M8, Montreal, QC, Canada aut NATIONALLICENCE 773 0- International Journal on Document Analysis and Recognition (IJDAR) Springer-Verlag 14/3(2011-09-01), 263-272 1433-2833 14:3<263 2011 14 10032 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer