Alphanumeric character recognition using a connectionist model with the pocket algorithm

Summary form only given. New results are presented which were obtained by applying a connectionist model with the pocket algorithm proposed by Gallant to problems in alphanumeric character recognition. An evaluation is made of the recognition (classification) capability of the connectionist model for 62 and 93 alphanumeric characters of a single font having different kinds of typeface quality and 76 alphanumeric characters of multiple fonts having the same typeface quality. A useful technique is proposed to distinguish between characters that closely resemble each other by using the structure information of characters. An activation criterion of output cells for character recognition is also proposed. In the recognition of characters having different typeface qualities, a markedly high degree of accuracy (99.96% maximum, 99.74% on average) of the individual fonts was attained. In the recognition of the 76 alphanumeric characters having multiple fonts, the degree of accuracy achieved was 99.64% maximum.<>