By Topic

Unconstrained handwritten digit VLSI recognition system based on combined neural networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Guoxing Li ; Dept. of Microelectron., Tsinghua Univ., Beijing, China ; Bingxue Shi

A totally unconstrained digit recognition system based on cellular neural network (CNN) and multilayer perceptron (MLP) and its analog-digital mixed mode VLSI implementation is presented in this paper. The CNN is used to extract CCD (connected component detector) feature from 24×24 normalized digit image in horizontal, vertical directions and two diagonal lines row by row. These features are fed into the two layers MLP in time sharing mode after proper compression. MLP has 60×20×10 structure with local connection. It consists of 10×10 neural processing unit (NPU) array which contains latches, weight generating circuits and computation circuits, switched current integrators, current threshold unit and control logic block. The weights are programmable and their control codes come from off-chip EPROM. This recognition system is very smart and effective, it can be implemented in standard digit CMOS technology

Published in:

Solid-State and Integrated Circuit Technology, 1998. Proceedings. 1998 5th International Conference on

Date of Conference: