@inproceedings{SchallSchambachFranz2018,
  author    = {Schall, Martin and Schambach, Marc-Peter and Franz, Matthias O.},
  title     = {Multi-Dimensional Connectionist Classification},
  booktitle = {13th IAPR International Workshop on Document Analysis Systems, 24 - 27. April 2018, Vienna, Austria},
  isbn      = {978-1-5386-3346-5},
  doi       = {10.1109/DAS.2018.36},
  institution = {Institut f{\"u}r Optische Systeme - IOS},
  pages     = {405 -- 410},
  year      = {2018},
  abstract  = {Offline handwriting recognition systems often use LSTM networks, trained with line- or word-images. Multi-line text makes it necessary to use segmentation to explicitly obtain these images. Skewed, curved, overlapping, incorrectly written text, or noise can lead to errors during segmentation of multi-line text and reduces the overall recognition capacity of the system. Last year has seen the introduction of deep learning methods capable of segmentation-free recognition of whole paragraphs. Our method uses Conditional Random Fields to represent text and align it with the network output to calculate a loss function for training. Experiments are promising and show that the technique is capable of training a LSTM multi-line text recognition system.},
  language  = {en}
}