Enhancing Cursive Word Recognition Performance by the Integration of All the Available Information

Segmentation-by-recognition is a successful approach for recognizing cursively handwritten words. Its main strength is that the interdependence of strokes forming a letter is correctly taken into account by the use of a character recognizer, that evaluates an aggregate of strokes (character hypothesis) as a whole. However, a straightforward implementation of such an approach would fail to take into account the dependencies of each character hypothesis with the adjacent hypotheses and with global characteristics of the image, like the position of upperline and baseline, the average dimensions of strokes, etc. This paper describes a cursive handwritten word recognition system in which recognition performance is enhanced by the use of several complementary sources of information, like the relationships of the strokes that make up a hypothesis among themselves and with the preceding strokes, the position of the hypothesis with respect to baseline and upperline, the statistics of the number of strokes making up letters belonging to different classes, the dispersion of character data around the different codevectors used to measure distances, the plausibility for a hypothesis of being a spurious stroke (extra ink). Experimental results are presented, putting into evidence the contribution of each source of information to the overall performance.