|
|
|
|
| LEADER |
01000caa a22002652c 4500 |
| 001 |
NLM274415178 |
| 003 |
DE-627 |
| 005 |
20250222020300.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231225s2018 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TPAMI.2017.2732978
|2 doi
|
| 028 |
5 |
2 |
|a pubmed25n0914.xml
|
| 035 |
|
|
|a (DE-627)NLM274415178
|
| 035 |
|
|
|a (NLM)28767364
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Xie, Zecheng
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition
|
| 264 |
|
1 |
|c 2018
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Revised 20.11.2019
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a Online handwritten Chinese text recognition (OHCTR) is a challenging problem as it involves a large-scale character set, ambiguous segmentation, and variable-length input sequences. In this paper, we exploit the outstanding capability of path signature to translate online pen-tip trajectories into informative signature feature maps, successfully capturing the analytic and geometric properties of pen strokes with strong local invariance and robustness. A multi-spatial-context fully convolutional recurrent network (MC-FCRN) is proposed to exploit the multiple spatial contexts from the signature feature maps and generate a prediction sequence while completely avoiding the difficult segmentation problem. Furthermore, an implicit language model is developed to make predictions based on semantic context within a predicting feature sequence, providing a new perspective for incorporating lexicon constraints and prior knowledge about a certain language in the recognition procedure. Experiments on two standard benchmarks, Dataset-CASIA and Dataset-ICDAR, yielded outstanding results, with correct rates of 97.50 and 96.58 percent, respectively, which are significantly better than the best result reported thus far in the literature
|
| 650 |
|
4 |
|a Journal Article
|
| 650 |
|
4 |
|a Research Support, Non-U.S. Gov't
|
| 700 |
1 |
|
|a Sun, Zenghui
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Jin, Lianwen
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Ni, Hao
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Lyons, Terry
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 40(2018), 8 vom: 03. Aug., Seite 1903-1917
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnas
|
| 773 |
1 |
8 |
|g volume:40
|g year:2018
|g number:8
|g day:03
|g month:08
|g pages:1903-1917
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TPAMI.2017.2732978
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 40
|j 2018
|e 8
|b 03
|c 08
|h 1903-1917
|