GRAPHICAL-DATA-PROCESSING RESEARCH STUDY AND EXPERIMENTAL INVESTIGATION

Abstract

The report describes the continuing development of preprocessing, classification, and context analysis techniques for hand-printed text, which are advancing at an accelerating pace. Experiments were continued with the Piecewise-Linear learning machine, using the outputs of two preprocessors: the PREP 24A simulation of the 1024-image optical preprocessor, and the CALMMASK preprocessor, which employs both edge-detecting and corner-detecting masks. A new low test error rate for classification was achieved on hand-printed alphabets of FORTRAN characters. Statistics of the performance of the learning machine during a single testing iteration are presented, and shed light on several important questions, such as the distribution of rankings of the desired character category when it is not in first place. A discussion of the preprocessing methods used in the topological approach to preprocessing and classification is begun. The initial development of a FORTRAN syntax analyzer is described. A milestone was reached with the passage of a small sample of actual FORTRAN text from a coding sheet through the scanning, preprocessing, classification, and syntax-analysis programs.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1967
Accession Number
AD0650926

Entities

People

  • J. H. Munson
  • P. E. Hart
  • R. O. Duda

Organizations

  • SRI International

Tags

Communities of Interest

  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Alphabets
  • Analyzers
  • Character Recognition
  • Computer Programming
  • Computer Programs
  • Computers
  • Data Processing
  • Decision Theory
  • Detectors
  • Dynamic Programming
  • Information Science
  • Iterations
  • Language
  • Pattern Recognition
  • Preprocessing
  • Recognition
  • Statistics

Fields of Study

  • Computer science

Readers

  • Computer Programming and Software Development.
  • Computer Science.
  • Neural Network Machine Learning.