Handwriting recognition using a reduced character method and neural nets

Nikolaos G. Bourbakis; Cris Koutsougeras; A. Jameel

doi:10.1117/12.205260

28 March 1995 Handwriting recognition using a reduced character method and neural nets

Nikolaos G. Bourbakis, Cris Koutsougeras, A. Jameel

Proceedings Volume 2424, Nonlinear Image Processing VI; (1995) https://doi.org/10.1117/12.205260
Event: IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, 1995, San Jose, CA, United States

Abstract

This paper deals with the recognition of handwriting text character by using a reduced character methodology and neural nets (BNN, RNN). The reduced characters methodology is based on the representation (mapping) of the text characters on a small size 2-D array of 12 X 9. For the recognition process each character is considered as a composition of `main' and `secondary' features. The main features are the necessary and important parts of a character for its recognition. The secondary (or artistic) features are the parts of a character which contribute to its various representations. The reduced character methodology presented in this paper attempts to prove that the recognition of a reduced size character provides a robust approach for recognition of handwritten text. The RNN approach for handwritten character recognition is based upon recurrent neural networks. The recurrent networks have a feedback mechanism. The feedback mechanism acts to integrate new values of feature vector with their predecessors. The output is supervised according to a target function. These networks can deal with inputs and outputs that are explicit functions of time. A new way of associating shape information was used, which gives very consistent results for handwritten character recognition. In this scheme the `shadow' each character was considered to find was the distances between the margins of the character. The distances are normalized with respect to the maximum distance in the entire shape to minimize the effect of disproportionately formed characters. For this effort two neural networks and an attributed graph approach are used and their results are compared on a set of 5000 handwritten characters.

Citation Download Citation

Nikolaos G. Bourbakis, Cris Koutsougeras, and A. Jameel "Handwriting recognition using a reduced character method and neural nets", Proc. SPIE 2424, Nonlinear Image Processing VI, (28 March 1995); https://doi.org/10.1117/12.205260

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