This paper presents the on-line handwriting recognition system NPen++ developed at the University of Karlsruhe and the Carnegie Mellon University. The NPen++ recognition engine is based on a Multi-State Time Delay Neural Network and yields recognition rates from 96% for a 5000 word dictionary

this report we will describe the work we have been doing within the NPen

This paper investigates the cooperation of on-line and off-line handwriting word recognition systems. Our goal is to improve a mature on-line recognition system by exploiting the complementary information present in the off-line representation built from on-line signal. After describing the on-line

The main goal of this chapter is to make an overview of new advances in on-line handwriting recognition systems, electronic ink management systems, open problems, new challenges and new perspectives. In the recent years, several papers dealing with surveys or states of the

State-of-the-art on-line handwriting recognition systems should be able to handle dictionary sizes of at least 25,000 words or more to be useful for real-world applications. Using dictionaries of this size requires fast search techniques to achieve reasonable recognition times. In this paper we

notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the on-line

In this paper we present a new multiple classifier system (MCS) for recognizing notes written on a whiteboard. This MCS combines one off-line and two on-line handwriting recognition systems derived from previous work. The recognizers are all based on Hidden Markov Models but vary in the way

then be performed on ordinary paper and pen trajectories are automatically extracted from image sequences recorded during the writing process. On the basis of this work we developed a complete video-based on-line handwriting recognition system. We will present the techniques applied for pen tracking, pre

Writer independent, large vocabulary on-line handwriting recognition systems require robust input representations, which make optimal use of the dynamic writing information, i.e. the temporal ordering of the sampled data points. In this paper we describe an input representation for cursive

This paper presents results of an effort to use the dynamic information present in an on-line handwriting signal. On-line handwriting recognition systems often ignore most of the dynamic information available in the signal. They commonly go to the extent of retaining the order of points being