The DTW Data Distribution over a Grid Computing Architecture

In this paper; we show how grid computing can further speedup Dynamic Time Warping (DTW) algorithm. More specifically, we present experimental results of the data distribution of the Arabic printed cursive OCR based on the Dynamic Time Warping (DTW) algorithm over the Scientific Research Tunisian Grid (SRTG). In fact, the Arabic printed cursive OCR based on the DTW algorithm provides very interesting recognition and segmentation rates. Among the advantages of the DTW algorithm, is its ability to achieve properly and simultaneously the recognition and the segmentation of connected or cursive characters from within a reference library of isolated characters. Unfortunately, the big amount of computing to be achieved during the recognition process makes the DTW execution time very slow and hence restricts its utilization. Conducted experiments show and confirm that grid computing presents a very interesting framework to speedup the DTW execution time. In addition, we found that the speedup and the efficiency factors increase with the size of the Arabic text.

[1]  Minjie Zhang,et al.  Agent-Based Grid Computing , 2008, Computational Intelligence: A Compendium.

[2]  Raúl Rojas,et al.  A survey on recognition of on-line handwritten mathematical notation , 2007 .

[3]  Abdelfettah Belghith,et al.  A multipurpose multi-agent system based on a loosely coupled architecture to speedup the DTW algorithm for Arabic printed cursive OCR , 2005, The 3rd ACS/IEEE International Conference onComputer Systems and Applications, 2005..

[4]  Jean-Luc Gauvain,et al.  A dynamic programming processor for speech recognition , 1989 .

[5]  S. Kanoun.,et al.  Reconnaissance d'images de textes arabes par approche affixale , 2004 .

[6]  Ibm Redbooks Introduction to Grid Computing With Globus , 2003 .

[7]  Neil W. Bergmann,et al.  An Arabic optical character recognition system using recognition-based segmentation , 2001, Pattern Recognit..

[8]  King-Sun Fu,et al.  VLSI architecture for dynamic time-warp recognition of handwritten symbols , 1986, IEEE Trans. Acoust. Speech Signal Process..

[9]  Adnan Amin,et al.  Off-line Arabic character recognition: the state of the art , 1998, Pattern Recognit..

[10]  Edson Cáceres,et al.  Parallel dynamic programming for solving the string editing problem on a CGM/BSP , 2002, SPAA '02.

[11]  Erkki Oja,et al.  Experiments with adaptation strategies for a prototype-based recognition system for isolated handwritten characters , 2001, International Journal on Document Analysis and Recognition.

[12]  C. V. Jawahar,et al.  Model-Based Annotation of Online Handwritten Datasets , 2006 .

[13]  Maher Khemakhem Reconnaissance de caractères imprimés par comparaison dynamique , 1987 .

[14]  Michael D. Brown,et al.  An algorithm for connected word recognition , 1982, ICASSP.

[15]  Steven Tuecke,et al.  The Anatomy of the Grid , 2003 .