Generating a training corpus for OCR post-correction using encoder-decoder model

International audienceIn this paper we present a novel approach to the automatic correction of OCR-induced orthographic errors in a given text. While current systems depend heavily on large training corpora or exter- nal information, such as domain-specific lexicons or confidence scores from the OCR process, our system only requires a small amount of relatively clean training data from a representative corpus to learn a character-based statistical language model using Bidirectional Long Short- Term Memory Networks (biLSTMs). We demonstrate the versatility and adaptability of our system on different text corpora with varying degrees of textual noise, in- cluding a real-life OCR corpus in the med- ical domain