A Connectionist Recognizer For On-Line Cursive Handwriting Recognition (0)
| Venue: | Proc. ICASSP'94 |
| Citations: | 13 - 2 self |
BibTeX
@INPROCEEDINGS{Manke_aconnectionist,
author = {Stefan Manke and Ulrich Bodenhausen},
title = {A Connectionist Recognizer For On-Line Cursive Handwriting Recognition},
booktitle = {Proc. ICASSP'94},
year = {},
pages = {633--636}
}
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Abstract
In this paper we show how the Multi-State Time Delay Neural Network (MS-TDNN), which is already used successfully in continuous speech recognition tasks, can be applied both to online single character and cursive (continuous) handwriting recognition. The MS-TDNN integrates the high accuracy single character recognition capabilities of a TDNN with a non-linear time alignment procedure (dynamic time warping algorithm) for finding stroke and character boundaries in isolated, handwritten characters and words. In this approach each character is modelled by up to 3 different states and words are represented as a sequence of these characters. We describe the basic MS-TDNN architecture and the input features used in this paper, and present results (up to 97.7% word recognition rate) both on writer dependent/ independent, single character recognition tasks and writer dependent, cursive handwriting tasks with varying vocabulary sizes up to 20000 words. 1. INTRODUCTION This paper describes a con...
