Enhancement Of Reading Accuracy By Multiple Data Integration

Kangsuk Lee

doi:10.1117/12.952615

24 July 1989 Enhancement Of Reading Accuracy By Multiple Data Integration

Kangsuk Lee

Proceedings Volume 1074, Imaging Workstations; (1989) https://doi.org/10.1117/12.952615
Event: OE/LASE '89, 1989, Los Angeles, CA, United States

Abstract

In this paper, a multiple sensor integration technique with neural network learning algorithms is presented which can enhance the reading accuracy of the hand-written numerals. Many document reading applications involve hand-written numerals in a predetermined location on a form, and in many cases, critical data is redundantly described. The amount of a personal check is one such case which is written redundantly in numerals and in alphabetical form. Information from two optical character recognition modules, one specialized for digits and one for words, is combined to yield an enhanced recognition of the amount. The combination can be accomplished by a decision tree with "if-then" rules, but by simply fusing two or more sets of sensor data in a single expanded neural net, the same functionality can be expected with a much reduced system cost. Experimental results of fusing two neural nets to enhance overall recognition performance using a controlled data set are presented.

Citation Download Citation

Kangsuk Lee "Enhancement Of Reading Accuracy By Multiple Data Integration", Proc. SPIE 1074, Imaging Workstations, (24 July 1989); https://doi.org/10.1117/12.952615

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available