Computing That Serves

How did we get to where we are in DIA and OCR, and where are we now?


Thursday, February 2, 2012 - 10:00am


George Nagy

Professor Emeritus, Rensselaer Polytechnic Institute


Dave Embley and Bill Barrett

Some properties of pattern classifiers used for character recognition are presented.
Possibilities for  improving accuracy by from exploiting various types of context
– language, data, shape – are illustrated. Further improvements are proposed
through adaptation and field classification. The presentation is intended to be accessible,
though not necessarily interesting, to those without a background
in machine learning, digital image analysis and optical character recognition.


George Nagy graduated from McGill University in Engineering Physics (fencing and chess). He earned his MS at McGill by solving Euler’s Second Equation for the hysteresis motor. He was awarded the PhD at Cornell University in 1962 for helping Frank Rosenblatt build Tobermory, a sixteen-foot, four-layer neural network for speech recognition. After a short postdoc he worked on character recognition and remote sensing at IBM Yorktown (he claims credit for IBM’s growth during this period). He devoted a reverse sabbatical at the Université de Montréal to recording pulse trains from cats’ medial geniculate nuclei. In 1972 he joined the Department of Computer Science at the University of Nebraska where he dabbled in computational geometry, GIS and HCI. Since 1985 he has been Professor of Computer Engineering at RPI in Troy, NY. Nagy’s credits in document analysis include Chinese character recognition with Dick Casey, “self-corrective” character recognition with Glen Shelton (with a reprise twenty-eight years later with Henry Baird), character recognition via cipher substitution with Casey, Sharad Seth, and Tin Ho, growing X-Y trees with Seth, table interpretation with Dave Embley, Mukkai Krishnamoorthy, Dan Lopresti and Seth, modeling random-phase noise with Prateek Sarkar and Lopresti, style-constrained classification with Sarkar, Hiromichi Fujisawa, Cheng-Lin Liu and Harsha Veeramachaneni, and recently paper-based election systems research with Lopresti and Elisa Barney Smith. In his spare time Nagy enjoys skiing, sailing, and writing prolix surveys.