KEYWORDS: Optical character recognition, Scanners, Computing systems, Image processing, Printing, Data conversion, Machine learning, Medical research, Data processing, Human-machine interfaces
In spite of a hundredfold decrease in the cost of relevant technologies, the role of document image processing systems is
gradually declining due to the transition to an on-line world. Nevertheless, in some high-volume applications, document
image processing software still saves millions of dollars by accelerating workflow, and similarly large savings could be
realized by more effective automation of the multitude of low-volume personal document conversions. While potential
cost savings, based on estimates of costs and values, are a driving force for new developments, quantifying such savings
is difficult. The most important trend is that the cost of computing resources for DIA is becoming insignificant compared
to the associated labor costs. An econometric treatment of document processing complements traditional performance
evaluation, which focuses on assessing the correctness of the results produced by document conversion software.
Researchers should look beyond the error rate for advancing both production and personal document conversion.
The World Wide Web is a vast information resource which can be useful for validating the results produced by document recognizers. Three computational steps are involved, all of them challenging: (1) use the recognition results in a Web search to retrieve Web pages that contain information similar to that in the document, (2) identify the relevant portions of the retrieved Web pages, and (3) analyze these relevant portions to determine what corrections (if any) should be made to the recognition result. We have conducted exploratory implementations of steps (1) and (2) in the business-card domain: we use fields of the business card to retrieve Web pages and identify the most relevant portions of those Web pages. In some cases, this information appears suitable for correcting OCR errors in the business card fields. In other cases, the approach fails due to stale information: when business cards are several years old and the business-card holder has changed jobs, then websites (such as the home page or company website) no longer contain information matching that on the business card. Our exploratory results indicate that in some domains it may be possible to develop effective means of querying the Web with recognition results, and to use this information to correct the recognition results and/or detect that the information is stale.
In image analysis, low-level recognition of the primitives plays a very important role. Once the primitives of the image are recognized, depending on the application, many types of analyses can take place. It is likely that associated with each object or primitive is a set of possible interpretations, herein referred to as the label set. The low-level recognizer may associate a probability with each label in the label set. We can use the constraints of the application domain to reduce the ambiguity in the object's identity. This process is variously termed constraint satisfaction, labeling, or relaxation. In this paper, we focus on the discrete form of relaxation. Our contribution lies in the development of a graph-rewriting approach which does not assume the degree of localness is high. We apply our approach to the recognition of music notation, where non-local interactions between primitives must be used in order to reduce ambiguity in the identity of the primitives. We use graph-rewriting rules to express not only binary constraints, but also higher-order notational constraints.
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