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Dissecting multi-line handwriting for multi-dimensional connectionist classification

  • 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.

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Metadaten
Author:Martin Schall, Marc-Peter Schambach, Matthias O. FranzORCiDGND
DOI:https://doi.org/10.1109/ICDAR.2019.00015
ISBN:978-1-7281-3015-6
ISBN:978-1-7281-3014-9
Parent Title (English):15th International Conference on Document Analysis and Recognition (ICDAR 2019), 20-25 September, Sydney, Australia
Publisher:IEEE
Document Type:Conference Proceeding
Language:English
Year of Publication:2020
Release Date:2020/01/15
Pagenumber:38
First Page:31
Note:
Volltextzugriff für Angehörige der Hochschule Konstanz möglich
Institutes:Institut für Optische Systeme - IOS
Relevance:Keine peer reviewed Publikation (Wissenschaftlicher Artikel und Aufsatz, Proceeding, Artikel in Tagungsband)
Open Access?:Nein
Licence (English):License LogoLizenzbedingungen IEEE