Extracting and indexing meaningful contents from degraded documents, like historical ones, is a challenging problem. Existing analysis systems usually rely on a manual correction of results during the post-processing stage, and cannot make use of external information to adapt their response. This paper presents how an existing document analysis system can be easily adapted to enable an efficient interaction during the analysis stage, and benefit from external information. We identify the minimal architecture required, and we detail the two complimentary interaction models we propose: a directed interaction model which allows to handle cases where errors can be automatically detected, and a spontaneous interaction model which permits to cope with the other cases. Both models are asynchronous to prevent the human operator or the system from waiting for each other during document processing. They are based on a common foundation which uses standard exception-like mechanisms to implement error detection, correction and recovery aspects. Our system was tested on several tasks. For instance, for the transcription of handwritten words in documents dating from the 18th century, where we were able to diminish the human workload by 28% for an overall recognition rate of 80%.
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