Length estimation of digit strings using a neural network with structure-based features
Accurate length estimation is very helpful for the suc- cessful segmentation and recognition of connected digit strings, in particular, for an off-line recognition system. However, little work has been done in this area due to the difficulties involved. A length es- timation approach is presented as a part of our automatic off-line digit recognition system. The kernel of our approach is a neural network estimator with a set of structure-based features as the in- puts. The system outputs are a set of fuzzy membership grades reflecting the degrees of an input digit string of having different lengths. Experimental results on National Institute of Standards and Technology (NIST) Special Database 3 and other derived digit strings shows that our approach can achieve an about 99.4% cor- rect estimation if the best two estimations are considered. © 1998 SPIE and IS&T. (S1017-9909(98)00901-5)