Chafic Mokbel

TL;DR: A 1D HMM offline handwriting recognition system employing an analytical approach supported by a set of robust language independent features extracted on binary images and learns character models without character pre-segmentation.

TL;DR: In this paper, the speech signal is modelled by means of a hidden Markov model and, at each instant t: equalization filters are constituted in association with the paths in the Markov sense at instant t; at least a plurality of the equalization filtering filters are applied to the sound frames to obtain, at instant T, an utterance probability for each of the paths respectively associated with the equalisation filters applied, and the filtered frame supplied by the selected equalization filter is selected as the equalized frame.

TL;DR: An overview of recent bibliographic references dealing with speech processing in mobile terminals is given and a fairly large list of references taken from many conferences proceedings and journals are given and commented.

TL;DR: The results show that the combination of classifiers performs better than a single classifier dealing with slant-corrected images and that the approach is robust for a wide range of orientation angles.

TL;DR: The main component of this combined system is an HMM-based recognizer which considers dynamic and contextual information for a better modeling of writing units and significantly improves recognition.