The authors have designed a writer-adaptable character recognition system for online characters entered on a touch terminal. It is based on a Time Delay Neural Network (TDNN) that is pre-trained on examples from many writers to recognize digits and uppercase letters. The TDNN without its last layer serves as a preprocessor for an optimal hyperplane classifier that can be easily retrained to peculiar writing styles. This combination allows for fast writer-dependent learning of new letters and symbols. The system is memory and speed efficient.>

A method for computer-aided cleaning of undesirable patterns in large training databases has been developed. The method uses the trainable classifier itself, to point out patterns that are suspicious, and should be checked by the human supervisor. While suspicious patterns that are meaningless or mislabeled are considered garbage, and removed from the database, the remaining patterns, like ambiguous or atypical, represent valid patterns that are hard to learn and should be kept in the database. By using the method of pattern cleaning, combined with an emphasizing scheme applied on the patterns that are hard to learn, the error rate on the test set has been reduced by half, in the case of the database of handwritten lowercase characters entered on a touch terminal. The classifier is based on a time delay neural network (TDNN).>