Object orientation detection and character recognition using optimal feedforward network and Kohonen's feature map

Nazife Baykal; Nese Yalabik

doi:10.1117/12.140010

16 September 1992 Object orientation detection and character recognition using optimal feedforward network and Kohonen's feature map

Nazife Baykal, Nese Yalabik

Author Affiliations +

Proceedings Volume 1709, Applications of Artificial Neural Networks III; (1992) https://doi.org/10.1117/12.140010
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

A neural network model, namely, Kohonen's Feature Map, together with the optimal feedforward network is used for variable font machine printed character recognition with tolerance to rotation, shift in position, and size errors. The determination of object orientation is found using the many rotated versions of individual symbols. Orientations are detected from printed text, but no knowledge of the context is used. The optimal Bayesian detector is derived, and it is shown that the optimal detector has the form of a feedforward network. This network together with the learning vector quantization (LVQ) approach is able to implement an inspection system which determines the orientation of the fonts. After the size normalization, rotation, and component finding process as a preprocessing step, the text becomes the input for the feature map. The feature map is trained first in an unsupervised manner. The algorithm is then adapted for supervised learning using improved LVQ technique. Rectangular and minimal spanning tree (MST) neighborhood topologies are experimented with. The results are encouraging, 87% of the characters of various fonts are correctly recognized even though the pattern is distorted in shape and transformed in a shift, size, and rotation invariant manner. Experimental results and comparisons are described.

Citation Download Citation

Nazife Baykal and Nese Yalabik "Object orientation detection and character recognition using optimal feedforward network and Kohonen's feature map", Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); https://doi.org/10.1117/12.140010

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