The Use of Dynamic Writing Information in a Connectionist On-Line Cursive Handwriting Recognition System
Abstract
In this paper we present NPen ++, a connectionist system for writer independent, large vocabulary on-line cursive handwriting recognition. This system combines a robust input representation, which preserves the dynamic writing information, with a neural network architecture, a so called Multi-State Time Delay Neural Network (MS-TDNN), which integrates rec.ognition and segmen(cid:173) tation in a single framework. Our preprocessing transforms the original coordinate sequence into a (still temporal) sequence offea(cid:173) ture vectors, which combine strictly local features, like curvature or writing direction, with a bitmap-like representation of the co(cid:173) ordinate's proximity. The MS-TDNN architecture is well suited for handling temporal sequences as provided by this input rep(cid:173) resentation. Our system is tested both on writer dependent and writer independent tasks with vocabulary sizes ranging from 400 up to 20,000 words. For example, on a 20,000 word vocabulary we achieve word recognition rates up to 88.9% (writer dependent) and 84.1 % (writer independent) without using any language models.
Cite
Text
Manke et al. "The Use of Dynamic Writing Information in a Connectionist On-Line Cursive Handwriting Recognition System." Neural Information Processing Systems, 1994.Markdown
[Manke et al. "The Use of Dynamic Writing Information in a Connectionist On-Line Cursive Handwriting Recognition System." Neural Information Processing Systems, 1994.](https://mlanthology.org/neurips/1994/manke1994neurips-use/)BibTeX
@inproceedings{manke1994neurips-use,
title = {{The Use of Dynamic Writing Information in a Connectionist On-Line Cursive Handwriting Recognition System}},
author = {Manke, Stefan and Finke, Michael and Waibel, Alex},
booktitle = {Neural Information Processing Systems},
year = {1994},
pages = {1093-1100},
url = {https://mlanthology.org/neurips/1994/manke1994neurips-use/}
}