@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} }