分散法による英数字の認識能力

抄録

Various learning algorithms for neural networks have recently appeared and have stimulated applications of neural networks in new problem areas. This paper presents new results obtained by applying the distributed method proposed by Gallant to problems in alphanumeric character recognition. This method uses a distributed neural network with the Pocket Algorithm which is a modification of Perceptron Learning. The authors evaluates the recognition (classification) capability of the distributed method for 62 and 93 alphanumeric characters of a single font having different kinds of typeface quality and 76 alphanumeric characters of multiple fonts (12 types of fonts) having the same typeface quality. Furthermore, we propose a useful technique to distinguish between characters that closely resemble each other by using the structure information of characters. We also propose an activation criterion of output cells for character recognition.
In the recognition of alphanumeric characters having different kinds of typeface quality, a markedly high degree of recognition accuracy (99.96% maximum, 99.74% on average) of individual font quality was attained. In the 76 alphanumeric character recognition of the multiple font, a very high degree of recognition (99.64% maximum) was also achieved. The relations among the recognition rate and the number of items of training data, the number of intermediate cells, and the number of training iterations are considered in this paper.