Thomas M. Breuel

Personalized search

A contextual computing approach may prove a breakthrough in personalized search efficiency.

Robust least-square-baseline finding using a branch and bound algorithm

Many document analysis and OCR systems depend on precise identification of page rotation, as well as the reliable identification of text lines. This paper presents a new algorithm to address both problems. It uses a branch-and-bound approach to globally optimal line finding and simultaneously models the baseline and the descender line under a Gaussian error/robust least square model. Results of applying the algorithm to documents in the University of Washington Database 2 are presented.

Modeling the sample distribution for clustering OCR

The paper re-examines a well-known technique in OCR, recognition by clustering followed by cryptanalysis, from a Bayesian perspective. The advantage of such techniques is that they are font-independent, but they appear not to have offered competitive performance with other pattern recognition techniques in the past. The analysis presented in this paper suggests an approach to OCR that is based on modeling the sample distribution as a mixture of Gaussians. Results suggest that such an approach may combine the advantages of cluster- based OCR with the performance of traditional classification algorithms.