The Use of Dynamic Writing Information in a Connectionist On-Line Cursive Handwriting Recognition System

w ai bel CO) cs. cm u. ed u 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-tation in a single framework. Our preprocessing transforms the original coordinate sequence into a (still temporal) sequence offea-ture vectors, which combine strictly local features, like curvature or writing direction, with a bitmap-like representation of the co-ordinate's proximity. The MS-TDNN architecture is well suited for handling temporal sequences as provided by this input rep-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