TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Schall, Martin
A1  - Schambach, Marc-Peter
A1  - Franz, Matthias O.
T1  - Dissecting multi-line handwriting for multi-dimensional connectionist classification
T2  - 15th International Conference on Document Analysis and Recognition (ICDAR 2019), 20-25 September, Sydney, Australia
N2  - Multi-Dimensional Connectionist Classification is amethod for weakly supervised training of Deep Neural Networksfor segmentation-free multi-line offline handwriting recognition.MDCC  applies  Conditional  Random  Fields  as  an  alignmentfunction for this task. We discuss the structure and patterns ofhandwritten text that can be used for building a CRF. Since CRFsare  cyclic  graphical  models,  we  have  to  resort  to  approximateinference when calculating the alignment of multi-line text duringtraining, here in the form of Loopy Belief Propagation. This workconcludes with experimental results for transcribing small multi-line samples from the IAM Offline Handwriting DB which showthat MDCC is a competitive methodology.
Y1  - 2020
SN  - 978-1-7281-3015-6
SB  - 978-1-7281-3015-6
SN  - 978-1-7281-3014-9
SB  - 978-1-7281-3014-9
U6  - https://doi.org/10.1109/ICDAR.2019.00015
DO  - https://doi.org/10.1109/ICDAR.2019.00015
N1  - Volltextzugriff für Angehörige der Hochschule Konstanz möglich
SP  - 31
S1  - 38
PB  - IEEE
ER  -