Comparison of Massively Parallel Hand-Print Segmenters

摘要

NIST has developed a massively parallel hand-print recognition system that allowscomponents to be interchanged. Using this system, three different character segmentation algorithms have been developed and studied. They are blob coloring, histogramming, and a hybrid of the two. The blob coloring method uses connected components to isolate characters. The histogramming method locates linear spaces, which may be slanted, to segment characters. The hybrid method is an augmented histogramming method that incorporates statistically adaptive rules to decide when a histogrammed item is too large and applies blob coloring to further segment the difficult item. The hardware configuration is a serial host computer with a 1024 processor Single Instruction Multiple Data (SIMD) machine attached to it. The data used in this comparison is 'NIST Special Database 1' which contains 2100 forms from different writers where each form contains 130 digit characters distributed across 28 fields. This gives a potential 273,000 characters to be segmented. Running the massively parallel system across the 2100 forms, blob coloring required 2.1 seconds per form with an accuracy of 97.5%, histogramming required 14.4 seconds with an accuracy of 95.3%, and the hybrid method required 13.2 seconds with an accuracy of 95.4%. The results of this comparison show that the blob coloring method on a SIMD architecture is superior.