Making Two Vast Historical Manuscript Collections Searchable and Extracting Meaningful Textual Features Through LargeScale Probabilistic Indexing Textual access to large collections of digitized images remains unfeasible because usually they lack transcripts. Transcribing such collections is in turn typically unattainable in terms of costs. However, the use of probabilistic indices can facilitate textual accessing with only moderate demands of resources. Besides allowing effortless information retrieval, it will be shown that probabilistic indices can also be used to estimate textual features of the indexed but otherwise untranscribed collections, such as running words and Zipfu0027s curves. Complete probabilistic indices have been recently produced for two iconic large collections: Bentham (90K images) and Spanish Golden Age Theater (40K images). To show the repercussion of making these collections searchable, we provide accessing statistics gathered through their corresponding search interfaces. To the best of our knowledge this is the first publication of large collections of untranscribed manuscripts which are now publicly accessible for effective and efficient textual access.

Music Symbol Sequence Indexing in Medieval Plainchant Manuscripts Huge amounts of musical manuscripts are preserved in cathedrals, abbeys, and archives. However, without reliable transcripts, their contents are inaccessible. Manual transcription is unaffordable for large collections, and current automatic technologiesxe2x80x94such as Optical Music Recognition or Handwritten Music Recognitionxe2x80x94do not provide sufficient accuracy for a fullyautomatic scenario. In many cases, perfect transcripts are not really needed, given that contentbased search with some degree of reliability would already be extremely useful. Spotting just single music symbols is rather useless (most of the symbols generally appear in all pages); instead, helpful search targets are melodic patterns, which typically correspond to music symbol sequences. We explore approaches for accurate retrieval of melodic patterns, represented by music symbol sequences, from collections of Medieval plainchant manuscripts. Our statistical framework, based on the use of convolutional recurrent neural networks and probabilistic indices, is shown to be useful for retrieving music patterns which appear frequently in this untranscribed images, yielding an Average Precision of 86 %.