An Enhanced HMM Topology in an LBA Framework for the Recognition of Handwritten Numeral Strings

In this study we evaluate different HMM topologies in terms of recognition of handwritten numeral strings by considering the framework of the Level Building Algorithm (LBA). By including an end-state in a left-to-right HMM structure we observe a significant improvement in the string recognition performance since it provides a better definition of the segmentation cuts by the LBA. In addition, this end-state allows us the use of a two-step training mechanism with the objective of integrating handwriting-specific knowledge into the numeral models to obtain a more accurate representation of numeral strings. The contextual information regarding the interaction between adjacent numerals in strings (spaces, overlapping and touching) is modeled in a pause model built into the numeral HMMs. This has shown to be a promising approach even though it is really dependent on the training database.