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Multi-Dimensional Connectionist Classification : Reading Text in One Step

  • 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.

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Author:Martin Schall, Marc-Peter Schambach, Matthias O. FranzORCiDGND
Parent Title (English):13th IAPR International Workshop on Document Analysis Systems, 24 - 27. April 2018, Vienna, Austria
Document Type:Conference Proceeding
Year of Publication:2018
Release Date:2019/01/08
First Page:405
Last Page:410
Institutes:Institut für Optische Systeme - IOS
Open Access?:Ja
Relevance:Keine peer reviewed Publikation (Wissenschaftlicher Artikel und Aufsatz, Proceeding, Artikel in Tagungsband)
Licence (German):License LogoKeine CC-Lizenz - Es gilt der Veröffentlichungsvertrag für Publikationen