This abstract has been accepted for Digital Humanities in the Nordic Countries 2nd Conference, http://dhn2017.eu/
In the Danish Newspaper Archive you can search and view 26 million newspaper pages. The search engine uses OCR (optical character recognition) from scanned pages but often the software converting the scanned images to text makes reading errors. As a result the search engine will miss matching words due to OCR error. Since many of our newspapers are old and the scans/microfilms is also low quality, the resulting OCR constitutes a substantial problem. In addition, the OCR converter performs poorly with old font types such as fraktur.
One way to find OCR errors is by using the unsupervised Word2Vec learning algorithm. This algorithm identifies words that appear in similar contexts. For a corpus with perfect spelling the algorithm will detect similar words synonyms, conjugations, declensions etc. In the case of a corpus with OCR errors the Word2Vec algorithm will find the misspellings of a given word either from bad OCR or in some cases journalists. A given word appears in similar contexts despite its misspellings and is identified by its context. For this to work the Word2Vec algorithm requires a huge corpus and for the newspapers we had 140GB of raw text.
Given the words returned by Word2Vec we use a Danish dictionary to remove the same word in different grammatical forms. The remaining words are filtered by a similarity measure using an extended version of Levenshtein distance taking the length of the word and an idempotent normalization taking frequent one and two character OCR errors into account.
Example: Let’s say you use the Word2Vec to find words for banana and it returns: hanana, bananas, apple, orange. Remove bananas using the (English) dictionary since this is not an OCR error. For the three remaining words only hanana is close to banana and it is thus the only misspelling of banana found in this example. The Word2Vec algorithm does not know how a words is spelled/misspelled, it only uses the semantic and syntactic context.
This method is not an automatic OCR error corrector and cannot output the corrected OCR. But when searching it will appear as if you are searching in an OCR corrected text corpus. Single word searches on the full corpus gives an increase from 3% to 20% in the number of results returned. Preliminary tests on the full corpus shows only relative few false positives among the additional results returned, thus increasing recall substantially without a decline in precision.
The advantage of this approach is a quick win with minimum impact on a search engine  based on low quality OCR. The algorithm generates a text file with synonyms that can be used by the search engine. Not only single words but also phrase search with highlighting works out of the box. An OCR correction demo using Word2Vec on the Danish newspaper corpus is available on the Labs pages of The State And University Library, Denmark.
 Mediestream, The Danish digitized newspaper archive.
 SOLR or Elasticsearch etc.
 Mikolov et al., Efficient Estimation of Word Representations in Vector Space
 OCR error detection demo (change word parameter in URL)
 Labs for State And University Library, Denmark