Fully convolutional network with dilated convolutions for handwritten text line segmentation
| Autor: | Sébastien Adam, Yann Soullard, Thierry Paquet, Clément Chatelain, Christopher Kermorvant, Guillaume Renton |
|---|---|
| Přispěvatelé: | Equipe Apprentissage (DocApp - LITIS), Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Université Le Havre Normandie (ULH), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA), Teklia |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] 020206 networking & telecommunications Pattern recognition 02 engineering and technology Text recognition Resolution (logic) Computer Science Applications [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation Computer Vision and Pattern Recognition Artificial intelligence Deconvolution Line (text file) Representation (mathematics) business Software |
| Zdroj: | International Journal on Document Analysis and Recognition International Journal on Document Analysis and Recognition, Springer Verlag, In press, ⟨10.1007/s10032-018-0304-3⟩ |
| ISSN: | 1433-2833 1433-2825 |
| DOI: | 10.1007/s10032-018-0304-3⟩ |
| Popis: | We present a learning-based method for handwritten text line segmentation in document images. Our approach relies on a variant of deep fully convolutional networks (FCNs) with dilated convolutions. Dilated convolutions allow to never reduce the input resolution and produce a pixel-level labeling. The FCN is trained to identify X-height labeling as text line representation, which has many advantages for text recognition. We show that our approach outperforms the most popular variants of FCN, based on deconvolution or unpooling layers, on a public dataset. We also provide results investigating various settings, and we conclude with a comparison of our model with recent approaches defined as part of the cBAD ( https://scriptnet.iit.demokritos.gr/competitions/5/ ) international competition, leading us to a 91.3% F-measure. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink