Off-Line Cursive Script Word Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Surface Interpolation Using Hierarchical Basis Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
LeRec: a NN/HMM hybrid for on-line handwriting recognition
Neural Computation
Generalized Projections: A Tool for Cursive Handwriting Normalization
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Towards Automatic Video-based Whiteboard Reading
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Handwriting beautification using token means
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
| Hi-index | 0.00 |
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.