Generation of learning samples for historical handwriting recognition using image degradation
Andreas Fischer|Muriel Visani|van Cuong Kieu|Ching Y. Suen
Historical documents pose challenging problems for training handwriting recognition systems. Besides the high variability of character shapes inherent to all handwriting, image quality can also differ greatly, instance due faded ink, ink bleed-through, wrinkled and stained parchment. Especially when only few learning samples are available, it is difficult incorporate this in morphological models. In paper, we investigate use degradation generate synthetic historical recognition. With respect three models, report significant improvements accuracy with hidden Markov models on medieval Saint Gall Parzival data sets.
HAL (Le Centre pour la Communication Scientifique Directe)|HAL (Le Centre pour la Communication Scientifique Directe)|HAL (Le Centre pour la Communication Scientifique Directe)|HAL (Le Centre pour la Communication Scientifique Directe)