Filtering segmentation cuts for digit string recognition

In this paper we propose a method to evaluate segmentation cuts for handwritten touching digits. The idea of this method is to work as a filter in segmentation-based recognition system. This kind of system usually rely on over-segmentation methods, where several segmentation hypotheses are created for each touching group of digits and then assessed by a general-purpose classifier. The novelty of the proposed methodology lies in the fact that unnecessary segmentation cuts can be identified without any attempt of classification by a general-purpose classifier, reducing the number of paths in a segmentation graph, what can consequently lead to a reduction in computational cost. An cost-based approach using ROC (receiver operating characteristics) was deployed to optimize the filter. Experimental results show that the filter can eliminate up to 83% of the unnecessary segmentation hypothesis and increase the overall performance of the system.