Evaluation of lexicon size variations on a verification and rejection system based on SVM,for accurate and robust recognition of handwritten words

摘要

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%).