A system for segmenting and recognising totally unconstrained handwritten numeral strings

We propose a system for segmentation and recognition of totally unconstrained handwritten numeral strings. The system is built upon a number of components, namely, a presegmentation module, an isolated numeral recognizer, a segmentation-free module and a merging module. Presegmentation consists in dividing the input numeral string image into groups of numerals each of which represents an integer number of numerals. For each group, the actual number of numerals and their identity are then determined by a cascade of two recognition-based tests: isolated numeral and segmentation-free. The last one is able to recognise a numeral group of any length. All results from all groups are eventually merged yielding the final interpretation of the input numeral string. We also introduce the concept of dummy symbol in order to overcome the problem of noisy parts that cannot be eliminated by standard filtering algorithms. Preliminary experiments on totally unconstrained data from the CEDAR database yielded a segmentation rate of 94.5% and a recognition rate of 84.2% at string level. These results compare favorably to other published methods.