The transcription of handwritten words remains a still challenging and difficult task. When processing full
pages, approaches are limited by the trade-off between automatic recognition errors and the tedious aspect of
human user verification. In this article, we present our investigations to improve the capabilities of an automatic
recognizer, so as to be able to reject unknown words (not to take wrong decisions) while correctly rejecting (i.e.
to recognize as much as possible from the lexicon of known words).
This is the active research topic of developing a verification system that optimize the trade-off between
performance and reliability. To minimize the recognition errors, a verification system is usually used to accept
or reject the hypotheses produced by an existing recognition system. Thus, we re-use our novel verification
architecture1 here: the recognition hypotheses are re-scored by a set of support vector machines, and validated
by a verification mechanism based on multiple rejection thresholds. In order to tune these (class-dependent)
rejection thresholds, an algorithm based on dynamic programming has been proposed which focus on maximizing
the recognition rate for a given error rate.
Experiments have been carried out on the RIMES database in three steps. The first two showed that this
approach results in a performance superior or equal to other state-of-the-art rejection methods. We focus here on
the third one showing that this verification system also greatly improves results of keywords extraction in a set
of handwritten words, with a strong robustness to lexicon size variations (21 lexicons have been tested from 167
entries up to 5,600 entries) which is particularly relevant to our application context cooperating with humans,
and only made possible thanks to the rejection ability of this proposed system. The proposed verification system,
compared to a HMM with simple rejection, improves on average the recognition rate by 57% (resp. 33% and
21%) for a given error rate of 1% (resp. 5% and 10%).