Iterated complete path search is a general speedup method for dynamic programming which was invented by Gary Kopec and further developed in these papers. It can be applied to inference in many stochastic models, including segmental HMMs.

Proceedings of IS&T/SPIE Electronic Imaging 2001: Document Recognition and Retrieval VIII, January 2001.

The computation time of Document Image Decoding can be significantly reduced by employing heuristics in the search for the best decoding of a text line. By using a cheap upper bound on template match scores, up to 99.9% of the potential template matches can be avoided. In the Iterated Complete Path method, template matches are performed only along the best path found by dynamic programming on each iteration. When the best path stabilizes, the decoding is optimal and no more template matches need be performed. Computation can be further reduced in this scheme by exploiting the incremental nature of the Viterbi iterations. Because only a few trellis edge weights have changed since the last iteration, most of the backpointers do not need to be updated. We describe how to quickly identify these backpointers, without forfeiting optimality of the path. Together these improvements provide a 30x speedup over previous implementations of Document Image Decoding.

Proceedings of the IAPR 2001 International Conference Document Analysis and Recognition (ICDAR 2001), September 2001.

It has been shown that the computation time of Document Image Decoding can be significantly reduced by employing heuristics in the search for the best decoding of a text line. In the Iterated Complete Path (ICP) method, template matches are performed only along the best path found by dynamic programming on each iteration. When the best path stabilizes, the decoding is optimal and no more template matches need be performed. In this way, only a tiny fraction of potential template matches must be evaluated, and the computation time is typically dominated by the evaluation of the initial heuristic upper-bound for each template at each location in the image.

The time to compute this bound depends on the resolution at which the matching scores are found. At lower resolution, the heuristic computation is reduced, but because a weaker bound is used, the number of Viterbi iterations is increased. We present the optimal (lowest upper bound) heuristic for any degree of subsampling of multilevel template and/or interpolation, for use in text line decoding with ICP. The optimal degree of subsampling depends on image quality, but it is typically found that a small amount of template subsampling is effective in reducing the overall decoding time.

Thomas P Minka Last modified: Tue Nov 01 17:42:31 GMT 2005