EXPERIMENTS ON NEURAL NET RECOGNITION OF SPOKEN AND WRITTEN TEXT

The problems are discussed of the recognition of handprinted and spoken digits and the handprinted and spoken English alphabet. Four such experiments were conducted and the results were compared to a conventional nearest-neighbor classifier trained on the same data. Results indicate that neural networks and nearest-neighbor classifiers perform at near the same level of accuracy. For each task, a critical number of neurons can be determined experimentally which yields highest recognition accuracy with least hardware. This number can also measure the classification efficiency of the input feature encoder. Several techniques for optimizing the performance of layered networks are discussed. A constant level added to the input signal biases patterns into the range where the learning rate is highest. Eliminating near-zero weights after learning results in little loss of accuracy. Finally, a novel handwriting encoder is described.