@inproceedings{ac0dd8b36db4488a8416d75185e31864,
title = "Handwritten digit recognition with binary optical perceptron",
abstract = "Binary weights are favored in electronic and optical hardware implementations of neural networks as they lead to improved system speeds. Optical neural networks based on fast ferroelectric liquid crystal binary level devices can benefit from the many orders of magnitudes improved liquid crystal response times. An optimized learning algorithm for all-positive perceptrons is simulated on a limited data set of handwritten digits and the resultant network implemented optically. First, gray-scale and then binary inputs and weights are used in recall mode. On comparing the results for the example data set, the binarized inputs and weights network shows almost no loss in performance.",
author = "I. Saxena and P. Moerland and E. Fiesler and A. Pourzand",
year = "1997",
doi = "https://doi.org/10.1007/bfb0020323",
language = "English",
isbn = "3540636315",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "1253--1258",
editor = "Wulfram Gerstner and Alain Germond and Martin Hasler and Jean-Daniel Nicoud",
booktitle = "Artificial Neural Networks - ICANN 1997 - 7th International Conference, Proceeedings",
address = "Germany",
note = "7th International Conference on Artificial Neural Networks, ICANN 1997 ; Conference date: 08-10-1997 Through 10-10-1997",
}