Training with noise is equivalent to Tikhonov regularization
Neural Computation
A Statistical, Nonparametric Methodology for Document Degradation Model Validation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generation of Synthetic Training Data for an HMM-based Handwriting Recognition System
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
A Line Drawings Degradation Model for Performance Characterization
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
N-Gram Language Models for Offline Handwritten Text Recognition
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Text line segmentation of historical documents: a survey
International Journal on Document Analysis and Recognition
Special issue on the analysis of historical documents
International Journal on Document Analysis and Recognition
Geometric Rectification of Camera-Captured Document Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Low quality document image modeling and enhancement
International Journal on Document Analysis and Recognition
Towards an omnilingual word retrieval system for ancient manuscripts
Pattern Recognition
Language Model Integration for the Recognition of Handwritten Medieval Documents
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Ground truth creation for handwriting recognition in historical documents
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
International Journal on Document Analysis and Recognition
Transcription alignment of Latin manuscripts using hidden Markov models
Proceedings of the 2011 Workshop on Historical Document Imaging and Processing
Lexicon-free handwritten word spotting using character HMMs
Pattern Recognition Letters
Synthesizing queries for handwritten word image retrieval
Pattern Recognition
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Historical documents pose challenging problems for training handwriting recognition systems.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 differ 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.