An Optimized Multi-stream Decoding Algorithm for Handwritten Word Recognition

Author

Kessentini, Yousri ; Paquet, Thierry ; Guermazi, Ahmed

Author_Institution

Lab. LITIS EA 4108, Univ. de Rouen, St. Etienne du Rouvray, France

fYear

2011

fDate

18-21 Sept. 2011

Firstpage

192

Lastpage

196

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.

Keywords

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; Computational complexity; Computational modeling; Databases; Decoding; Handwriting recognition; Hidden Markov models; Decoding; Handwriting recognition; Multi-stream HMM; Two-level;

fLanguage

English

Publisher

ieee

Conference_Titel

Document Analysis and Recognition (ICDAR), 2011 International Conference on

Conference_Location

Beijing

ISSN

1520-5363

Print_ISBN

978-1-4577-1350-7

Electronic_ISBN

1520-5363

Type

conf

DOI

10.1109/ICDAR.2011.47

Filename

6065302