Improvement in Handwritten Numeral String Recognition by Slant Normalization and Contextual Information

This work describes a way of enhancing handwritten numeral string recognition by considering slant normalization and contextual information to train an implicit segmentation-based system. A word slant normalization method is modified in order to improve the results for handwritten numeral strings. We assume that each connected component (CC) in the string has its own slant. The slant and contour length of each CC are used for obtaining the mean slant of the string. Both the original and modified methods are evaluated by means of some interesting analyses on the NIST SD19 database. These analyses show (a) the positive impact of slant correction on the number of overlapping numerals in strings, and (b) the difference in normalizing isolated numerals based on the slant estimated from their own images and the slant estimated from their original string images. Slant normalization and contextual information regarding string slant and digit size variations within the string are used to train numeral HMMs. Preliminary string recognition results, produced by a system under construction, are shown.