Generation of Learning Samples for Historical Handwriting Recognition Using Image Degradation

Andreas Fischer (1) , Muriel Visani (2) , van Cuong Kieu (3) , Suen Ching Y. (4)

1 CENPARMI
2 L3I - Laboratoire Informatique, Image et Interaction - EA 2118
3 IDDC
4 CENPARMI

Historical documents pose challenging problems for training handwriting recognition systems.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 difficult to incorporate this variability in the morphological character models. In this paper, we investigate the use of image degradation to generate 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.

Mots clés: Historical Documents; Handwriting Recognition; Learning Sample Generation; Synthetic Images; Image Degradation; Hidden Markov Models

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

Andreas Fischer, Muriel Visani, van Cuong Kieu, Suen Ching Y.. Generation of Learning Samples for Historical Handwriting Recognition Using Image Degradation. Proceedings of the Workshop on Historical Document Imaging and Processing 2013 (HIP 2013), Aug 2013, Washington DC, United States. pp.73-79. ⟨hal-00859329⟩