  Query by String Keyword Spotting (KWS) is here considered as a key technology
for indexing large collections of handwritten text images to allow fast textual
access to the contents of these collections. Under this perspective, a
probabilistic framework for lexicon-based KWS in text images is presented. The
presentation aims at providing a tutorial view that helps to understand the
relations between classical statements of KWS and the relative challenges
entailed by these statements. More specifically, the development of the
proposed framework makes it self-evident that word recognition or
classification implicitly or explicitly underlies any formulation of KWS.
Moreover, it clearly suggests that the same statistical models and training
methods successfully used for handwriting text recognition can advantageously
be used also for KWS, even though KWS does not generally require or rely on any
kind of previously produced image transcripts. These ideas are developed into a
specific, probabilistically sound approach for segmentation-free,
lexicon-based, query-by-string KWS. Experiments carried out using this approach
are presented, which support the consistency and general interest of the
proposed framework. Several datasets, traditionally used for KWS benchmarking
are considered, with results significantly better than those previously
published for these datasets. In addition, results on two new, larger
handwritten text image datasets are reported, showing the great potential of
the methods proposed in this paper for indexing and textual search in large
collections of handwritten documents.
