A Digital Simulation of an Aided Adaptive Character Reading Machine

Paul Baran, Gerald Estrin

Expert InsightsPublished 1960

A simulation, on an IBM 709 computer, of a pattern-recognition system using an initial man-machine learning phase. Transformations on a deformed set of 48 samples of each of ten numerals are used to form separation filters, while a second set of 480 similarly varied numerals serve as the "unknown" characters that are examined. Measured probability density distributions of the inked areas of all characters are established, and a weighted stencil or filter is created to distinguish each character relative to the possible set of characters. This experiment demonstrates the extent to which the actual value of the best "score of match" between the unknown and each character in the set provides confidence in recognition. Whenever the best score is too low, it is possible to call for more complex processes to aid recognition permitting the construction of recognition systems of greater accuracy than the basic reading mechanism.

Topics

Document Details

Citation

Chicago Manual of Style

Baran, Paul and Gerald Estrin, A Digital Simulation of an Aided Adaptive Character Reading Machine. Santa Monica, CA: RAND Corporation, 1960. https://www.rand.org/pubs/papers/P1989.html.
BibTeX RIS

This publication is part of the RAND paper series. The paper series was a product of RAND from 1948 to 2003 that captured speeches, memorials, and derivative research, usually prepared on authors' own time and meant to be the scholarly or scientific contribution of individual authors to their professional fields. Papers were less formal than reports and did not require rigorous peer review.

This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited; linking directly to this product page is encouraged. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial purposes. For information on reprint and reuse permissions, please visit www.rand.org/pubs/permissions.

RAND is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.