Application Of Image Processing Equipment With Multiprocessors To Line-Drawing Recognition

Hiroshi Ogawa; Etsuo Kawada; Naonori Ueda

doi:10.1117/12.976491

13 October 1987 Application Of Image Processing Equipment With Multiprocessors To Line-Drawing Recognition

Hiroshi Ogawa, Etsuo Kawada, Naonori Ueda

Proceedings Volume 0845, Visual Communications and Image Processing II; (1987) https://doi.org/10.1117/12.976491
Event: Cambridge Symposium on Optics in Medicine and Visual Image Processing, 1987, San Diego, CA, United States

Abstract

This paper describes image processing equipment developed for a data entry system aimed at creating a plant-record database. The equipment employs a multi-processor architecture allowing parallel processing of very large drawings by dividing them into sub-images. Image enhancement and binarization processing is executed on the equipment to improve the image quality of handwritten plant-record drawings. The recognition processing of symbols and line segments in the drawing is also implemented. The equipment is operated as a part of an experimental data entry system for Al-size handwritten plant-record drawings. It is confirmed that blur and faintness are eliminated in about 25 seconds, and the target symbols and line segments are recognized in about 15 minutes with an accuracy of over 90%.

Citation Download Citation

Hiroshi Ogawa, Etsuo Kawada, and Naonori Ueda "Application Of Image Processing Equipment With Multiprocessors To Line-Drawing Recognition", Proc. SPIE 0845, Visual Communications and Image Processing II, (13 October 1987); https://doi.org/10.1117/12.976491

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