13. A non-transitory computer readable medium having computer readable program code embodied therein, said computer readable program code adapted to be executed to implement a method for a plurality of at least partially superimposed fragments of input strokes on a computing device, the computing device comprising a processor, the non-transitory computer readable medium under control of the processor implementing the method comprising: determining the time order of input of at least sequential fragments; detecting the geometry of the input strokes in the at least sequential fragments; detecting the relative positions of the input strokes of the at least sequential fragments; determining from the determined time order and detected relative positions and geometries whether one or more of the input strokes of the at least sequential fragments combine to form one or more likely characters; classifying the fragments based on the determined likely characters; providing the classified fragments to a recognition engine for evaluation of character hypotheses based on the classified fragments; creating a segmentation graph based on the strokes of the classified fragments, wherein the segmentation graph includes nodes corresponding to character hypotheses; assigning a recognition score to each node of the segmentation graph based on a pattern classifier; and generating linguistic meaning of the input strokes based on the recognition scores and a language model.
13. A non-transitory computer readable medium having computer readable program code embodied therein, said computer readable program code adapted to be executed to implement a method for a plurality of at least partially superimposed fragments of input strokes on a computing device, the computing device comprising a processor, the non-transitory computer readable medium under control of the processor implementing the method comprising: determining the time order of input of at least sequential fragments; detecting the geometry of the input strokes in the at least sequential fragments; detecting the relative positions of the input strokes of the at least sequential fragments; determining from the determined time order and detected relative positions and geometries whether one or more of the input strokes of the at least sequential fragments combine to form one or more likely characters; classifying the fragments based on the determined likely characters; providing the classified fragments to a recognition engine for evaluation of character hypotheses based on the classified fragments; creating a segmentation graph based on the strokes of the classified fragments, wherein the segmentation graph includes nodes corresponding to character hypotheses; assigning a recognition score to each node of the segmentation graph based on a pattern classifier; and generating linguistic meaning of the input strokes based on the recognition scores and a language model. 15. A non-transitory computer readable medium according to claim 13 , wherein each classified fragment is defined to contain complete characters formed by the input strokes of one or more input fragments.