Two Methods to Improve Confidence Scores for Lexicon-Free Word Spotting in Handwritten Text

Two methods are presented to improve word confidence scores for Line-Level Query-by-String Lexicon-Free Keyword Spotting (KWS) in handwritten text images. The first one approaches true relevance probabilities by means of computations directly carried out on character lattices obtained from the lines images considered. The second method uses the same character lattices, but it obtains relevance scores by first computing frame-level character sequence scores which resemble the word posteriorgrams used in previous approaches for lexicon-based KWS. The first method results from a formal probabilistic derivation, which allow us to better understand and further develop the underlying ideas. The second one is less formal but, according with experiments presented in the paper, it obtains almost identical results with much lower computational cost. Moreover, in contrast with the first method, the second one allows to directly obtain accurate bounding boxes for the spotted words.