Geometric decision trees for optical character recognition (extended abstract)
George N. Sazaklis, Esther M. Arkin, Joseph S. B. Mitchell, Steven Skiena · 1997
A fundamental problem in computer vision is identifying which of a given set of geometric models is present in animage.Reconsider anapproach to model recognition basedon computing efficient strategies (decision trees) for 'probing" a scanned image of a typeset document, in order to perform fast and effective optical character recognition (OCR).We consider a "proben to be a simply computed local operator that can be applied to discriminate between two sets of possible models.By carefully constructing effective probes, and assembling them into a geometric decision tree, we have devised, implemented, and compared a variety of methods to perform OCR.In this paper, we present algorithms for probing strategies and decision tree construction, and we report experiment al results on the effectiveness of theae algorithms in identifying English characters and numerals in scanned images of printed pages of text.These algorithms are implemented as part of a system used by a document processing company (Syngen Corp.).