Generation of learning samples for historical handwriting recognition using image degradation

Andreas Fischer, Muriel Visani, Van Cuong Kieu, Ching Y. Suen
2013 Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing - HIP '13  
Historical documents pose challenging problems for training handwriting recognition systems. Besides the high variability of character shapes inherent to all handwriting, the image quality can also di↵er greatly, for instance due to faded ink, ink bleed-through, wrinkled and stained parchment. Especially when only few learning samples are available, it is di cult to incorporate this variability in the morphological character models. In this paper, we investigate the use of image degradation to
more » ... enerate synthetic learning samples for historical handwriting recognition. With respect to three image degradation models, we report significant improvements in accuracy for recognition with hidden Markov models on the medieval Saint Gall and Parzival data sets.
doi:10.1145/2501115.2501123 dblp:conf/icdar/FischerVKS13 fatcat:ve64wcdwfzgctm46bi7dvgdjqe