In this paper we describe the NPen ++ system for writer independent on-line handwriting recognition. This recognizer needs no training for a particular writer and can recognize any common writing style (cursive, hand-printed, or a mixture of both). The neural network architecture, which was originally proposed for continuous speech recognition tasks, and the preprocessing techniques of NPen ++ are designed to make heavy use of the dynamic writing information, i.e. the temporal sequence of data points recorded on a LCD tablet or digitizer. We present results for the writer independent recognition of isolated words. Tested on different dictionary sizes from 1,000 up to 100,000 words, recognition rates range from 98.0% for the 1,000 word dictionary to 91.4% on a 20,000 word dictionary and 82.9% for the 100,000 word dictionary. No language models are used to achieve these results. 1 Introduction The success and user acceptance of pen computing or multi-modal systems highly depends on ...

A critical feature of any computer system is its interface with the user. This has led to the development of user interface technologies such as mouse, touchscreen and penbased input devices. Since handwriting is one of the most familiar communication media, pen-based interfaces combined with automatic handwriting recognition offers a very easy and natural input method. Pen-based interfaces are also essential in mobile computing because they are scalable. Recent advances in pen-based hardware and wireless communication have been influential factors in the renewed interest in on-line recognition systems. On-line handwriting recognition is fundamentally a pattern classification task; the objective is to take an input pattern, the handwritten signal collected on-line via a digitizing device, and classify it as one of a pre-specified set of words (i.e., the system's lexicon). Because exact recognition is very difficult, a lexicon is used to constrain the recognition output to a known vocab...

Abstract. We present a structural analysis method for the recognition of on-line handwritten mathematical expressions based on a minimum spanning tree construction and symbol dominance. The method handles some layout irregularities frequently found in on-line handwritten formula recognition systems, like symbol overlapping and association of arguments of sum-like operators. It also handles arguments of operators with non-standard layouts, as well as tabular arrangements, like matrices. 1

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 handwriting, which combines this dynamic writing information with static bitmaps used in optical character recognition. This input representation is used with a connectionist recognizer, which is well suited for handling temporal sequences of patterns as provided by this kind of input representation. Our system has been tested on different cursive handwriting recognition tasks with vocabulary sizes up to 20000 words. We achieve recognition rates up to 99.5% on writer independent, single character recognition tasks and up to 98.1% on writer dependent, cursive handwriting tasks. 1 Introduction Several different preprocessing techniques both for optical character recognition (OCR) and on-line character...

The field of personal computing has begun to make a transition from the desktop to handheld devices, thereby requiring input paradigms that are more suited for single hand entry than a keyboard and recent developments in online handwriting recognition allow for such input modalities. Data entry using a pen forms a natural, convenient interface. The large number of writing styles and the variability between them makes the problem of writer-independent unconstrained handwriting recognition a very challenging pattern recognition problem. The state-of-the-art in online handwriting recognition is such that it has found practical success in very constrained problems. In this thesis, a method of identifying different writing styles, referred to as lexemes, is described. Approaches for constructing both non-parametric and parametric classifiers are described that take advantage of the identified lexemes to f...

Handwriting recognition has attracted enormous scientific interest because of its potential for improved man/machine interfaces. We have designed an on-line handwritten digit recognition system after the examination of different techniques based on statistical and neural pattern recognition approaches. We collected a digit database from 44 people. We use two representations. The dynamic representation is based on constant length feature vectors of equally distanced points on the pen trajectory. The static representation converts the dynamic information to an image similar to images used in off-line recognition tasks.Then, we tested the well known statistical classification method k-nearest neighbor (k-NN) and neural multi-layer perceptron (MLP) and recurrent networks using both representations. Classifiers trained with dynamic and static representations make misclassifications for different samples. We combine them first by forming a feature vector composed of dynamic and static repr...

We investigate techniques to combine multiple representations of a handwritten digit to increase classification accuracy without significantly increasing system complexity or recognition time. In pen-based recognition, the input is the dynamic movement of the pentip over the pressure sensitive tablet. There is also the image formed as a result of this movement. On a real-world database of handwritten digits containing more than 11,000 handwritten digits, we notice that the two multi-layer perceptron (MLP) based classifiers using these representations make errors on different patterns implying that a suitable combination of the two would lead to higher accuracy. We implement and compare voting, mixture of experts, stacking and cascading. Combining the two MLP classifiers we indeed get higher accuracy because the two classifiers/representations fail on different patterns. We especially advocate multistage cascading scheme where the second costlier image-based classifier is employed only in a small percentage of cases. 1.

this paper we discuss an on-line handwriting recognition system that employs signal preprocessing, feature invariance, and stochastic modeling with HMM's that incorporate a language model. Results are presented for the UNIPEN data

Abstract. This paper presents a system for recognizing sketched logic circuits in real-time and graphically simulating them afterwords. It has been developed for use in university and school education. Circuit gate symbols are recognized using a multilayer perceptron network. The simulation is fully controlled by hand-drawings, and the inputs to circuits can be defined by writing numbers next to them. In addition to the simulation of simple circuits, recursive circuits can also be handled by the system. Furthermore, clock elements can be added for the purpose of synchronization, and circuits can be stored to be reused as sub-circuits, allowing the user to build arbitrary complex configurations. The usability of the system has been tested in a small video-taped laboratory test. 1

Word shape information can be helpful in handwriting recognition. It can be used by a segmentation based recognizer to verify the results of the recognition of individual segmented letters. It is often one of the main features used by wholistic recognizers. This paper describes extraction of the word shape information through identification of the middle zone, then ascenders and descenders. Extracted information is applied both in a bottom-up and top-down fashion resulting in a verification mechanism for existing segmentation based system and a new wholistic recognizer. The wholistic recognizer is found relatively weak. However, when the two are combined, a significant improvement is observed. Recognition results are presented. 1. Introduction Zoning information is an important feature in cursive script recognition [8]. It allows distinction between letters which are very similar, either for human readers (e.g. "c" and "C"), or for specific recognition algorithms (e.g. "a" and "d" fo...