  In the field of document analysis and recognition using mobile devices for
capturing, and the field of object recognition in a video stream, an important
problem is determining the time when the capturing process should be stopped.
Efficient stopping influences not only the total time spent for performing
recognition and data entry, but the expected accuracy of the result as well.
This paper is directed on extending the stopping method based on next
integrated recognition result modelling, in order for it to be used within a
string result recognition model with per-character alternatives. The stopping
method and notes on its extension are described, and experimental evaluation is
performed on an open dataset MIDV-500. The method was compares with previously
published methods based on input observations clustering. The obtained results
indicate that the stopping method based on the next integrated result modelling
allows to achieve higher accuracy, even when compared with the best achievable
configuration of the competing methods.
