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 |
|---|---|
| URN: | urn:nbn:de:bsz:kon4-opus4-14348 |
| URL: | https://ieeexplore.ieee.org/document/8395230 |
| DOI: | https://doi.org/10.1109/DAS.2018.36 |
| ISBN: | 978-1-5386-3346-5 |
| Parent Title (English): | 13th IAPR International Workshop on Document Analysis Systems, 24 - 27. April 2018, Vienna, Austria |
| Publisher: | IEEE |
| Document Type: | Conference Proceeding |
| Language: | English |
| 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): |  Keine CC-Lizenz - Es gilt der Veröffentlichungsvertrag für Publikationen |
