The accuracy of Optical Character Recognition (OCR) technologies considerably impacts the way digital documents are indexed, accessed and exploited. During the last decades, OCR engines have been constantly improving and are today able to return exploitable results on mainstream documents. But in practice, digital libraries have on shelves many transcriptions with a quality below expectation. In fact, ancient documents with challenging layouts and various levels of conservation such as historical newspapers still resist to modern OCRs. Moreover, formerly digitized resources processed with out-dated OCRs are rarely re-sent through the latest state-of-the-art digitization pipeline, as priority is often given to the ever-growing masses of new arriving documents. In this context, OCR post-correction approaches, either used on former digitized documents or on fresh challenging documents, could strongly benefit digital libraries.