New Preprocessing Techniques for Handwritten Word Recognition

M. Blumenstein, C.K. Cheng, and X.Y. Liu (Australia)


Handwritten Word Recognition, Preprocessing, Skew Detection, Underline Removal


The research described in this paper focuses on the presentation of two novel preprocessing techniques for the task of off-line handwritten word recognition. A technique for the identification of straight and skewed underline noise is described along with a novel algorithm for detecting skew in handwritten words. The latter identifies skew by detecting the center of mass in each half of a word image. By hypothesizing a line between the two centres and by measuring the angle it makes with the x-axis, an angle for skew may be estimated. The algorithms are tested on the CEDAR benchmark database of handwritten cursive words. Results above 96% are reported for skew detection and underline removal.

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