Image-understanding approach to reading business transaction documents

Louis A. Oddo; James F. McNamara; Robert W. Smith

doi:10.1117/12.169466

25 February 1994 Image-understanding approach to reading business transaction documents

Louis A. Oddo, James F. McNamara, Robert W. Smith

Proceedings Volume 2103, 22nd AIPR Workshop: Interdisciplinary Computer Vision: Applications and Changing Needs; (1994) https://doi.org/10.1117/12.169466
Event: 22nd Applied Imagery Pattern Recognition Workshop, 1993, Washington, DC, United States

Abstract

One high-payoff applications of image character recognition (ICR) technology is the automatic processing of business transaction documents. For remittance types of transactions, these documents consist of checks and invoices. While the general layout of a check is consistent with respect to types of data fields present, there is variability in the locations of the data fields printed on the document. Furthermore, checks are printed using multiple fonts, character sizes, and line spacing on a single document. In contrast, invoice contents and layouts vary widely from business to business. The high degree of layout variability of business documents poses significant problems with respect to data field location and extraction in preparation for ICR. This paper describes an image understanding approach for locating and extracting data fields from various business documents. This approach is shown to be tolerant to image rotation, multiple fonts, multiple character sizes, and line spacing. Actual checks and invoices are processed using the described algorithms. The resulting field isolation and extraction performance, together with the resulting ICR read rates, are statistically analyzed and presented.

Citation Download Citation

Louis A. Oddo, James F. McNamara, and Robert W. Smith "Image-understanding approach to reading business transaction documents", Proc. SPIE 2103, 22nd AIPR Workshop: Interdisciplinary Computer Vision: Applications and Changing Needs, (25 February 1994); https://doi.org/10.1117/12.169466

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KEYWORDS

Optical character recognition

Image understanding

Image processing

Legal

Image resolution

Visualization

Algorithm development

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