5. An unconstrained cursive character handwritten word recognition system, comprising a processor including: an image processing module operable to process an image of a handwritten word of one or more characters, wherein the processing of the imaged word includes segmenting the imaged word into a finite number of segments and determining a sequence of the segments using an over-segmentation-relabeling algorithm, wherein each character includes one or more consecutive segments; a feature extraction module operable to derive a feature vector to represent feature information of one segment or a combination of several consecutive segments; and a classification module operable to determine an optimal string of one or more characters as composing the imaged word, wherein the classification module uses a continuous-discrete hybrid probability modeling of features toy determine a final symbol probability of whether a given feature vector is indicative of a given distinct character, wherein, in the continuous-discrete hybrid probability modeling of N features, the features N are separated into a first group N 1 and a second group N 2 , features of the first group N 1 are distributed using a continuous probability model to obtain a continuous distribution probability measure, features of the second group N 2 , are distributed using a discrete probability model given by equation (1) b j ⁡ ( O ) = ∏ i = 1 N 2 ⁢ ⁢ P ⁡ ( s i ) ( 1 ) wherein, in Equation (1), b j (O) is the discrete symbol probability distribution of the features of the second group N 2 for an observation O composed of the one segment or the combination of several consecutive segments, wherein P(s i ) is the probability of s i , and s i is the i-th feature of the observation, and wherein the continuous distribution probability measure and the discrete distribution probability measure b j (O) are multiplied and normalized to obtain the final symbol probability of whether a given feature vector is indicative of a given distinct character.
5. An unconstrained cursive character handwritten word recognition system, comprising a processor including: an image processing module operable to process an image of a handwritten word of one or more characters, wherein the processing of the imaged word includes segmenting the imaged word into a finite number of segments and determining a sequence of the segments using an over-segmentation-relabeling algorithm, wherein each character includes one or more consecutive segments; a feature extraction module operable to derive a feature vector to represent feature information of one segment or a combination of several consecutive segments; and a classification module operable to determine an optimal string of one or more characters as composing the imaged word, wherein the classification module uses a continuous-discrete hybrid probability modeling of features toy determine a final symbol probability of whether a given feature vector is indicative of a given distinct character, wherein, in the continuous-discrete hybrid probability modeling of N features, the features N are separated into a first group N 1 and a second group N 2 , features of the first group N 1 are distributed using a continuous probability model to obtain a continuous distribution probability measure, features of the second group N 2 , are distributed using a discrete probability model given by equation (1) b j ⁡ ( O ) = ∏ i = 1 N 2 ⁢ ⁢ P ⁡ ( s i ) ( 1 ) wherein, in Equation (1), b j (O) is the discrete symbol probability distribution of the features of the second group N 2 for an observation O composed of the one segment or the combination of several consecutive segments, wherein P(s i ) is the probability of s i , and s i is the i-th feature of the observation, and wherein the continuous distribution probability measure and the discrete distribution probability measure b j (O) are multiplied and normalized to obtain the final symbol probability of whether a given feature vector is indicative of a given distinct character. 12. The handwritten word recognition system of claim 5 , wherein the imaged word is an Arabic word.