Document analysis and recognition systems often fail to produce results with a sufficient quality level when processing old and damaged documents sets, and require manual corrections to improve results. This paper presents how, using the iterative analysis of document pages we recently proposed, we can implement a spontaneous interaction model, suitable for mass document processing. It enables human operators to detect and correct errors made by the automatic system, and reintegrates the corrections they made into subsequent analysis steps of the iterative analysis process. Thus, a page analyzer can reprocess erroneous parts and those which depend on them, avoiding the necessity to manually fix during post-processing all the consequences of errors made by the automatic system. After presenting the global system architecture and a prototype implementation of our proposal, we show that document model can be simply enriched to enable the spontaneous interaction model we propose. We present how to use it in a practical example to correct under-segmentation issues during the localization of numbers in documents from the 18th century. Evaluations we conducted on the example case show, on 50 pages containing 1637 numbers to localize, that the interaction model we propose can reduce human workload (29.8% less elements to provide) for a given target quality level when compared to a manual post-processing.