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Low-resource Post Processing of Noisy OCR Output for Historical Corpus Digitisation

We develop a post-processing system to efficiently correct errors from noisy optical character recognition (OCR) in a 2.7 million word Faroese corpus. 7.6% of the words in the original OCR text contain an error; fully manual correction would take thousands of hours due to the size of the corpus. Instead, our post-processing method applied to the Faroese corpus is projected to reduce the word error rate to 1.3% with around 65 hours of human annotator work. The foundation for generating corrected text is an HMM that learns patterns of OCR error and decodes noisy OCR character sequences into hypothesised correct language. A dictionary augments the HMM by contributing additional language knowledge, and a human annotator provides judgements in a small subset of cases that are identified as otherwise most prone to inaccurate output. An interactive workstation facilitates quick and accurate input for annotation. The entire toolkit is written in Python and is being made available for use in other low-resource languages where standard OCR technology falls short of desirable text quality. Supplementary analyses explore the impact of variable language resource availability and annotator time limitations on the end quality achievable with our toolkit.