Ink Normalization and Beautification
Patrice Y. Simard, Dave Steinkraus, Maneesh Agrawala
Abstract
Handwriting recognition is difficult because of the high variability of handwriting and because of segmentation errors. We propose an approach that reduces this variability without requiring letter segmentation. We build an ink extrema classifier which labels local minima of ink as {bottom, baseline, other} and maxima as {midline, top, other}. Despite the high variability of ink, the classifier is 86% accurate (with 0% rejection). We use the classifier information to normalize the ink. This is done by applying a "rubber sheet" warping followed by a "rubber rod" warping. Both warpings are computed using conjugate gradient methods. We display the normalization results on a few examples. This paper illustrates the pitfalls of ink normalization and "beautification ", when solved independently of letter recognition.