Part-Structured Inkball Models for One-Shot Handwritten Word Spotting

Many document collections of historical interest are handwritten and lack transcripts. Scholars need tools for high-quality information retrieval in such environments, preferably without the burden of extensive system training. This paper presents a novel approach to word spotting designed for manuscripts or degraded print that requires minimal initial training. It can infer a generative word appearance model from a single instance, and then use the model to retrieve similar words from arbitrary documents. An approximation to the retrieval statistic runs efficiently on graphics processing hardware. Tested on two standard data sets, the method compares favorably with prior results.