Abstract
We describe a system which can recognize digits and uppercase letters handprinted on a touch terminal. A character is input as a sequence of [x(t), y(t)] coordinates, subjected to very simple preprocessing, and then classified by a trainable neural network. The classifier is analogous to "time delay neural networks" previously applied to speech recognition. The network was trained on a set of 12,000 digits and uppercase letters, from approximately 250 different writers, and tested on 2500 such characters from other writers. Classification accuracy exceeded 96% on the test examples.
Original language | English (US) |
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Pages (from-to) | 105-119 |
Number of pages | 15 |
Journal | Pattern Recognition |
Volume | 24 |
Issue number | 2 |
DOIs | |
State | Published - 1991 |
Keywords
- Character recognition
- Handwritten characters
- Neural networks
- On-line character recognition
- Touch screen
- Touch terminal
ASJC Scopus subject areas
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence