TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Schall, Martin
A1  - Schambach, Marc-Peter
A1  - Franz, Matthias O.
T1  - Multi-Dimensional Connectionist Classification
BT  - Reading Text in One Step
T2  - 13th IAPR International Workshop on Document Analysis Systems, 24 - 27. April 2018, Vienna, Austria
N2  - 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.
Y1  - 2018
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:kon4-opus4-14348
UR  - https://ieeexplore.ieee.org/document/8395230
SN  - 978-1-5386-3346-5
SB  - 978-1-5386-3346-5
U6  - https://doi.org/10.1109/DAS.2018.36
DO  - https://doi.org/10.1109/DAS.2018.36
SP  - 405
EP  - 410
PB  - IEEE
ER  -