The most scientific papers dealing with handwriting recognition systems make statements relating to the recognition performance based on a forced-recognition rate. This rate describes the ratio between the number of the correct recognized samples and the number of all possible samples. For systems applied in real applications this rate is not very relevant. They have to work with a very low error-rate, which can be only achieved by introducing effective reject criteria. So the real interesting thing is a function describing the recognition rate in relation to a specific error rate, including implicitly a corresponding reject rate. This paper presents two approaches for handling rejects in a hidden Markov based handwriting recognition system. The features to determine a reject are values which are derived from the hidden Markov recognizer. One of the techniques relies on relative frequencies of those values, the other one utilizes standard classification techniques to train a reject decision unit, the reject classifier. Both methods are presented with some noteworthy results.
['Joachim M. Gloger', 'Alfred Kaltenmeier', 'Eberhard Mandler', 'L. Andrews']
Reject management in a handwriting recognition system