Name
Abstract | ||
|---|---|---|
This paper is focused on the optimization of the computational efficiency of a multi-stream word recognition system. The aim of this work is to optimize the multi-stream decoding step in order to reduce the recognition time and the complexity to allow combining a large number of streams. Two different multi-stream decoding strategies are compared based on two-level and HMM-recombination algorithms. Experiments carried out on public handwritten word databases show significant speed gains at decoding while keeping the same performances, in addition to new insights for combining a large number of streams. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICDAR.2011.47 | Document Analysis and Recognition |
Keywords | Field | DocType |
computational complexity,handwritten character recognition,hidden Markov models,image coding,HMM-recombination algorithms,complexity reduction,computational efficiency,handwritten word recognition,hidden Markov models,multistream word recognition system,optimized multistream decoding algorithm,public handwritten word databases,recognition time reduction,Decoding,Handwriting recognition,Multi-stream HMM,Two-level | Pattern recognition,Computer science,Word recognition,Image coding,Handwriting recognition,Algorithm,Speech recognition,Artificial intelligence,Decoding methods,Hidden Markov model,Intelligent word recognition,Computational complexity theory | Conference |
ISSN | ISBN | Citations |
1520-5363 E-ISBN : 978-0-7695-4520-2 | 978-0-7695-4520-2 | 0 |
PageRank | References | Authors |
0.34 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yousri Kessentini | 1 | 100 | 15.39 |
| Thierry Paquet | 2 | 565 | 56.65 |
| Ahmed Guermazi | 3 | 0 | 0.68 |