Most conventional document understanding systems use lexicons only in a postprocessing step to verify or correct character recognition results. The authors present a new approach to word recognition that uses a lexicon to "drive" the recognition process. Lexicon words are encoded in trie data structures, and recognition of a word image is done by searching a lexicon trie for a path whose node characters yield the best match to the word image. This approach has two important advantages. First, it is segmentation-free; there is no need to presegment the text image into isolated characters. Second, it performs recognition by verifying character hypotheses, as opposed to the classification method used in most conventional optical character recognition (OCR) systems. Hence, the recognition process is more efficient and the results are more accurate. They demonstrated the feasibility and the advantage of this approach with a lexicon size of more than 50000 words, on severely degraded images.

Segmentation is a key step in current OCR systems. It has been estimated that half the errors in character recognition are due to segmentation. A novel approach that performs OCR without the segmentation step was developed. The approach starts by extracting significant geometric features from the input document image of the page. Each feature then votes for the character that could have generated that feature. Thus, even if some of the features are occluded or lost due to degradation, the remaining features can successfully identify the character. In extreme cases, the degradation may be severe enough to prevent recognition of some of the characters in a word. In such cases, a lexicon-based word recognition technique is used to resolve ambiguity. Inexact matching and probabilistic evaluation used in the technique make it possible to identify the correct word, by detecting a partial set of characters. The authors first present an overview of their segmentation-free OCR system and then focus on the word recognition technique. Preliminary experimental results show that this is a very promising approach.>

When confronted with degraded images of text, however, the performance of many commercial OCR systems deteriorates severely. This occurs because all these systems rely on a segmentation step that is prone to error in the presence of image noise and printing artifacts. The authors present a novel OCR approach that overcomes this problem by eliminating the segmentation step altogether. This approach is based on the concept and techniques of occluded object recognition. To achieve high efficiency as well as robustness, they incorporate the notions of indexing and voting, and tailor them to the problem of OCR. Preliminary experimental results are given.>

The notion of uncertainty manipulation in robot navigation is applied to the task of model-based object recognition. Based on the fact that every detected object feature in an image must be associated with some uncertainty, the authors show how to iteratively refine the estimate of pose uncertainty of a hypothesized object when a new feature is matched, and how to dynamically determine a proper search window to look for the next model feature. The advantage of this method over the conventional static search window is demonstrated in an actual object recognition task.>

Three utility measures that can be used to judge the effectiveness of features in a model-based matching strategy are defined: detectability, reliability, and error rate. With the probability framework, it is shown how it is possible to analytically estimate the reliability of a hypothesis using the measures of the individual features. Based on these measures, the authors have formulated and experimentally verified the matching cost (expressed as execution time) of locating an instance of a model in an image. An algorithm is presented for the selection of optimal seed features of the model using the estimated cost of criterion.>