Length Estimation of Digit Strings

Accurate length estimation is very helpful for the successful segmentation and recognition of connected digit strings, in particular, for an oo-line recognition system. However, little work has been done in this area due to the diiculties involved. In this paper, a length estimation approach is presented as a part of an automatic oo-line digit recognition system. The kernel of our approach is a neural network estimator with a set of structure based features as the inputs. The system outputs are a set of fuzzy membership grades reeecting the degrees of an input digit string for having diierent lengths. Experimental results on NIST Special Database 3 and other derived digit strings shows that our approach can achieve about 99.4% correct estimation if the best two estimations are considered.