Ge. Kopec et M. Lomelin, SUPERVISED TEMPLATE ESTIMATION FOR DOCUMENT IMAGE DECODING, IEEE transactions on pattern analysis and machine intelligence, 19(12), 1997, pp. 1313-1324
An approach to supervised training of character templates from page im
ages and unaligned transcriptions is proposed. The template training p
roblem is formulated as one of constrained maximum likelihood paramete
r estimation within the document image decoding framework. This leads
to a three-phase iterative training algorithm consisting pf transcript
ion alignment, aligned template estimation (ATE), and channel estimati
on steps. The maximum likelihood ATE problem is shown to be NP-complet
e and, thus, an approximate solution approach is developed. An evaluat
ion of the training procedure in a document-specific decoding task, us
ing the University of Washington UW-II database of scanned technical j
ournal articles, is described.