Probabilistic Indexing for Fast and Effective Information Retrieval

This chapter provides details for the implementation of the different approaches proposed for PrIx under the probabilistic framework presented in Chapter 3, using the HTR models and tools described in Chapter 4. After a relatively brief presentation of the implementation of image-processing oriented methods, based on pixel-level posteriorgrams, in the rest of the chapter efficient algorithms are presented in detail which can be used to produce PrIxs under the HTR point of view. In both cases, the aim is to process large collections of text images off-line, so as to allow fast response to on-line queries with very low computing time complexity. To this end, for each image of the collection a series of sufficiently likely “spots” is extracted. Each spot contains a word or a character sequence, called (pseudo)word, along with the corresponding relevance probability and word position information. Two main approaches are discussed: lexicon-based and lexicon-free. The former allows simpler implementations and provides better search accuracy whenever the adopted lexicon provides enough coverage of the expected query words. The latter is not as accurate, but it is much more versatile since the words to be indexed are automatically “discovered” in the very images being indexed.