Share Email Print
cover

Proceedings Paper

Handwritten word recognition based on Fourier coefficients
Author(s): Gary F. Shartle; Steven K. Rogers; Dennis W. Ruck; Matthew Kabrisky; Mark A. O'Hair
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

A machine that can read unconstrained handwritten words remains an unsolved problem. For example, automatic entry of handwritten documents into a computer is yet to be accomplished. Most systems attempt to segment letters of a word and read words one character at a time. Segmenting a handwritten word is very difficult and often, the confidence of the results is low. Another method which avoids segmentation altogether is to treat each word as a whole. This research investigates the use of Fourier Transform coefficients, computed from the whole word, for the recognition of handwritten words. To test this concept, the particular pattern recognition problem studied consisted of classifying four handwritten words, `Buffalo', `Vegas', `Washington', and `City' from the SUNY post office database. Several feature subsets of the Fourier coefficients are examined. The best recognition performance of 76.2% was achieved when the Karhunen-Loeve transform was computed on the Fourier coefficients and those features were fed into a multilayer perceptron.

Paper Details

Date Published: 2 March 1994
PDF: 7 pages
Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); doi: 10.1117/12.169977
Show Author Affiliations
Gary F. Shartle, Air Force Institute of Technology (United States)
Steven K. Rogers, Air Force Institute of Technology (United States)
Dennis W. Ruck, Air Force Institute of Technology (United States)
Matthew Kabrisky, Air Force Institute of Technology (United States)
Mark A. O'Hair, Air Force Institute of Technology (United States)


Published in SPIE Proceedings Vol. 2243:
Applications of Artificial Neural Networks V
Steven K. Rogers; Dennis W. Ruck, Editor(s)

© SPIE. Terms of Use
Back to Top