Optical character recognition (OCR) is one of the most popular techniques used for
converting printed documents into machine-readable ones. While OCR engines can do well …

Automatic spelling correction has been receiving sustained research attention. Although
each article contains a brief introduction to the topic, there is a lack of work that would …

We present a Parallel Iterative Edit (PIE) model for the problem of local sequence
transduction arising in tasks like Grammatical error correction (GEC). Recent approaches …

The optical character recognition (OCR) quality of the historical part of the Finnish
newspaper and journal corpus is rather low for reliable search and scientific research on the …

For indexing the content of digitized historical texts, optical character recognition (OCR)
errors are a hampering problem. To explore the effectivity of new strategies for OCR post …

Optical character recognition (OCR) is crucial for a deeper access to historical collections.
OCR needs to account for orthographic variations, typefaces, or language evolution (ie, new …

Text normalization is an indispensable stage in processing noncanonical language from
natural sources, such as speech, social media or short text messages. Research in this field …

Named Entity Recognition (NER), search, classification and tagging of names and name like
frequent informational elements in texts, has become a standard information extraction …

This paper presents experiments on Optical character recognition (OCR) as a combination
of Ocropy software and data-driven spelling correction that uses Weighted Finite-State …

We show that the general problem of string transduction can be reduced to the problem of
sequence labeling. While character deletions and insertions are allowed in string …